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Location: United States, Los Angeles, CA Why Sony Interactive Entertainment? Sony Interactive Entertainment isn’t just the Best Place to Play — it’s also the Best Place to Work. Sony Interactive Entertainment (SIE) is the company behind the PlayStation brand. As a subsidiary of Sony Group Corporation, we’re part of a proud legacy of innovation and excellence. SIE is a dynamic technology company, delivering cutting-edge hardware and network services to more than 100 million people and an entertainment leader, home to some of the most beloved and recognizable intellectual properties (IP) in the world. Our role at SIE is to create and nurture the experiences under the PlayStation brand, a name synonymous with entertainment excellence and creativity. Senior Data Scientist, Product Usage & Value Lens (SBG / PBG Finance Partnership) At PlayStation, Data Science plays a critical role in shaping how we invest in, retain, and delight our global player base. Our Player Value Science team turns understanding of value mechanics into incrementality: we explain what drives value, where it is created or missed, and what to change, so growth can be created and protected. As a Senior Data Scientist, you will drive the development of advanced modelling and analytical approaches that influence high-stakes commercial and portfolio decisions at scale. Working closely with stakeholders across Finance, Strategy and Platform, you will work on complex and often ambiguous problems with a high degree of autonomy to shape how data informs strategy and investment decisions through a deeper understanding of player behaviour, product usage, and long-term value. This role combines deep technical expertise with strong commercial judgement. You will own the problem definition and approach, and success will be measured not only by the quality of your work, but by the adoption and impact on business outcomes. What You'll Do Design and deliver advanced modelling approaches that quantify player value, incremental impact, and long-term platform value, applying techniques such as causal inference, forecasting, optimization and machine learning where appropriate. Measure the incremental effect of launches, investments, and external market events, isolating true impact from what would have happened anyway, and translating that into an actionable understanding of where value is created. Build optimization and scenario models that inform long-range planning and investment decisions under real-world constraints. Own ambiguous problem spaces,  and break down complex business challenges into tractable, measurable solutions. Partner closely with cross-functional teams across Finance, Strategy and Platform to ensure value-driving products are robust, trusted and adopted, helping stakeholders understand how to recognise and attribute impact. Evaluate and select modelling approaches based on problem context, balancing rigour, performance, scalability, interpretability, and business impact. Mentor other data scientists and contribute to best practices across the team. Communicate complex modelling approaches, assumptions and outcomes clearly to technical and non-technical audiences, including Finance leadership. What We're Looking For You combine technical depth with a proactive, solution-oriented mindset, and the ability to influence strategy and inspire others. You thrive on ambiguous, high-value problems, using data to deepen PlayStation's understanding of its players and its portfolio, and inform decisions and strategies that drive sustainable growth. Proven industry experience delivering impactful, decision-grade analytical and modelling solutions in areas such as customer value, incrementality, forecasting, or investment decisioning. Experience with optimization or operations research approaches (for example resource allocation, scheduling, or constrained planning problems), or a strong quantitative foundation to pick these up quickly. Strong grounding in causal inference and impact measurement, with the judgement to critically assess assumptions, recognise when correlation is not enough and select approaches that are proportionate to the problem being solved. Experience owning projects end-to-end, from problem definition and selecting methods through to delivery and business adoption. Ability to work with large, complex datasets and translate them into scalable, trustworthy solutions. Exceptional stakeholder management and communication skills, with a track record of influencing commercial or finance partners and communicating results persuasively to both technical and non-technical audiences. Proficiency in Python and SQL, and comfort across the modern analytical and ML tooling ecosystem; experience with PySpark or equivalent distributed data processing tools. A strong quantitative background, typically a Master's or PhD in operations research, machine learning, statistics, economics, or a related field. Nice to Have Background in gaming, e-commerce, or subscription-based businesses. Experience partnering with Finance or commercial functions on portfolio, investment, or impact-attribution problems. Industry experience with experimentation frameworks and causal inference. Expertise in modern machine learning approaches, including applying techniques such as transformers, embeddings, or sequence-based models to real-world behavioural data. Experience working in agile, fast-paced environments At SIE, we consider several factors when setting each role’s base pay range, including the competitive benchmarking data for the market and geographic location. Please note that the base pay range may vary in line with our hybrid working policy and individual base pay will be determined based on job-related factors which may include knowledge, skills, experience, and location. In addition, this role is eligible for SIE’s top-tier benefits package that includes medical, dental, vision, matching 401(k), paid time off, wellness program and coveted employee discounts for Sony products. This role also may be eligible for a bonus package. Click here to learn more. The estimated base pay range for this role is listed below. $167,200 — $250,800 USD Please note, Sony Interactive Entertainment conducts background checks at the offer stage for all new employees (which may include criminal background checks for some roles) and will need to process personal information to support these checks. Please refer to our Candidate Privacy Notice (where applicable) for more information about what personal information we collect, how we use it, who we share it with, and your data protection rights. Equal Opportunity Statement: Sony is an Equal Opportunity Employer. All persons will receive consideration for employment without regard to gender (including gender identity, gender expression and gender reassignment), race (including colour, nationality, ethnic or national origin), religion or belief, marital or civil partnership status, disability, age, sexual orientation, pregnancy, maternity or parental status, trade union membership or membership in any other legally protected category. We strive to create an inclusive environment, empower employees and embrace diversity. We encourage everyone to respond. Sony Interactive Entertainment is a Fair Chance employer and qualified applicants with arrest and conviction records will be considered for employment.

Location: Portugal; Albania; Georgia; Greece; Bulgaria; Kosovo; Bosnia and Herzegovina; Armenia; Italy; Spain; Malta; Romania; Ireland; Croatia; Serbia; Montenegro Workplace: Remote ABOUT BLOCK LABS Block Labs is a premier technology studio operating at the bleeding edge of Web3, Artificial Intelligence, and iGaming. We don't just ship features; we engineer high-scale, production-grade platforms that power the next generation of digital products. We are a collective of senior engineers, product strategists, and builders who refuse to compromise on architecture. Whether we are designing autonomous multi-agent AI systems, building decentralized financial infrastructure, or architecting high-frequency iGaming platforms, our standard is excellence. We move fast, but we build for the long term. If you are looking to work alongside a team that values deep technical expertise, thoughtful system design, and product ownership, Block Labs is where you belong. THE ROLE Data & Intelligence now sits at the centre of several products we are developing, and we need a platform that is both dependable and capable of supporting more advanced intelligence over time. This role reflects that shift. We are designing a new data platform that will act as the backbone for everything from real time decisioning to predictive modelling. As a Data Platform Engineer in the Data Team, you will own the end-to-end real-time pipeline, serving data across a unified analytical warehouse and feature-serving layer. You are not building dashboards. You are engineering the commercial nervous system of a multi-tenant platform designed to scale from one operator to 10x with marginal infrastructure cost. KEY RESPONSIBILITIES: - Design, build, and maintain scalable data pipelines using AWS Glue (PySpark), or equivalent orchestration and transformation tools. - Engineer and optimise the ClickHouse warehouse for sub-second query performance across all back-offices. - Implement data contracts between back-office and the platform. Onboarding a new operator is a config change, not new tables, topics, or feature views. - Build the feature-serving layer providing pre-computed features to AI agents at millisecond latency. - Integrate with third-party databases, back-office APIs, and external systems (CRM, affiliates, acquisition platforms). - Establish monitoring, alerting, and maintenance procedures including pipeline health checks, freshness monitoring, anomaly detection, and data contract SLA enforcement. - Own CI/CD and infrastructure-as-code for data workloads. - Collaborate with data scientists, agent engineers, BI developers, and infrastructure teams to translate data requirements into reliable, production-grade pipelines. ABOUT YOU: - 3+ years building and operating production data pipelines at scale, with hands-on experience across both streaming and batch paradigms. - Expertise in Apache Kafka (or Amazon MSK): topic design, consumer group management, offset handling, schema registry operations, and production troubleshooting of lag, rebalancing, and throughput issues. - Strong SQL and warehouse engineering skills: experience with columnar analytical databases (ClickHouse strongly preferred, or similar: Druid, BigQuery, Redshift). - PySpark / Spark Streaming proficiency: writing transformation jobs that normalise, enrich, and enforce business rules on event streams. Experience with AWS Glue, Apache Airflow, or Apache NiFi is a strong plus. - Data modelling discipline: ability to design normalised, multi-tenant schemas where tenant isolation is a filter, not a fork. Experience with data contracts and schema governance. - CI/CD and infrastructure-as-code experience: automated testing of data pipelines, version-controlled deployments (CloudFormation, Terraform, or CDK), and familiarity with containerised workloads (ECS Fargate or Kubernetes). - Data quality and observability mindset: experience implementing pipeline health monitoring, automated data validation (Great Expectations or equivalent), freshness checks, and anomaly detection. NICE TO HAVE - Experience in iGaming, online casino, poker, or sportsbook platforms. - Exposure to blockchain or crypto-native transaction flows, including on-chain event ingestion, token-denominated accounting, or stablecoin settlement. - Comfortable operating in an AWS-native environment (MSK, Glue, S3, DynamoDB, ECS, IAM). You understand serverless tradeoffs and can size infrastructure for cost efficiency. - Feature store experience (SageMaker, Feast, or Tecton) building offline/online feature pipelines that serve ML models at inference time. - Prior work in regulated industries (financial services, gambling, fintech) where data lineage, auditability, and compliance are non-negotiable. - Experience migrating legacy query engines (Athena, Trino, Presto) to modern analytical warehouses with reconciliation frameworks to validate correctness. HOW WE WORK - Fully remote with asynchronous-first communication. EU time zone overlap is preferred. - Small, high-autonomy team within the Data function. You report to the Head of Data and co-ordinate with the AI, BI, and Infrastructure Teams. - Architecture decisions are documented and debated. You will participate in design reviews and own your domain decisions. - We build for multi-tenant scale from day one. Every pipeline, schema, and contract you ship must absorb a new operator without engineering effort. - On-call rotation will be established in the run phase. During the build phase, the focus is velocity with quality. No firefighting legacy systems. What kind of culture can I expect? Mature, mission-driven, and low-ego. We value clarity over noise, outcomes over theatrics, and pace without chaos. If you’re one of the smartest minds in your craft and want to build with other experts, you’ll feel at home here.

Задачи: - Разрабатывать и дообучать модели NLP/LLM: эмбеддинги, генерация, RAG-подходы. - Исследовать новые архитектуры, ставить эксперименты, проводить A/B-тесты. - Проектировать и развивать сервисы на базе языковых моделей: ассистенты, интеллектуальный поиск, генерация документов и кода. - Оптимизировать инференс и внедрять модели в продакшн совместно с ML и backend-командами. - Работать с данными: предобработка, векторизация, организационное хранение knowledge-баз. Требования: - 3+ лет коммерческого опыта в NLP/LLM. - Глубокая математика: ML, статистика, теория вероятностей. - Python + DS-стек: pandas/polars, numpy, scipy, scikit-learn. - Практика с PyTorch, Transformers, sentence-transformers, LangChain / LangGraph / LlamaIndex. - Опыт работы с моделями Qwen, DeepSeek, Llama, Mistral; SFT, LoRA, QLoRA, prompt-tuning. - Навыки работы с Docker, Kubernetes, CI/CD. - Умение настраивать и оптимизировать RAG-пайплайны. Будет плюсом: - Опыт в финтехе или корпоративных экосистемах. - Работа с Hadoop (Hive, PySpark), Greenplum, PostgreSQL. - Понимание интеграции LLM в бизнес-процессы. - Опыт работы в кросс-функциональных командах.

Location: Remote (Peru) At Livefront, we help companies design and build world-class digital products that command attention and inspire joy. We’ve helped household names like CVS, Samsung, General Mills, and Optum create experiences that have reached millions of people, and startups like HomeSpotter and Credly build entirely new businesses that challenge their industries’ status quo. We're looking for an outstanding Senior Databricks Data Engineer to join our growing data practice — someone who builds the data and AI foundations that digital products and intelligent experiences run on. This is a senior-level position with the opportunity to work remotely in Peru. Who you are You are a Databricks-focused Data Engineer who understands that great data platforms are only as valuable as the products, AI workflows, and experiences they enable. You bring deep, production-grade expertise across the Databricks platform and know how to connect platform capabilities to real business outcomes. You thrive in ambiguity and can quickly assess a client's data landscape to recommend and implement the right solutions. You understand that in consulting, your Databricks depth is most valuable when it connects platform capabilities to the products and experiences clients actually use — and you're as comfortable in a product design conversation as you are building a DLT pipeline. You excel at translating complex data challenges into clear technical requirements and can confidently navigate conversations with everyone from data scientists to executives. Your engineering principles are mature and grounded in real-world experience across various industries and scales. You have an interest in and a curiosity about data platforms and the latest advances in data technology. What you will be doing Design and build production data pipelines using Lakeflow Declarative Pipelines, Autoloader, and Structured Streaming, with end-to-end ownership of ingestion, transformation, data quality expectations, and CI/CD deployment via Declarative Automation Bundles. Architect and implement Lakehouse solutions on Databricks — medallion architecture, Delta Lake, Unity Catalog — tailored to the client's analytics, AI, and application needs. Build and maintain Databricks transformation layers — DLT pipelines, PySpark notebooks, and dbt — with data quality constraints and SLAs baked in. Design and maintain the data and AI foundations — Unity Catalog, Feature Store, MLflow, and Model Serving — that power production ML, agent workflows, and AI-enabled digital products. Collaborate with product and backend engineers to design data models, APIs, and application data contracts — ensuring the platform serves the product, not just the warehouse. Consult with clients to understand their data challenges, develop data strategies, and implement sustainable solutions. Adapt your approach based on project needs — sometimes leading data architecture discussions with clients, other times supporting internal teams with specialized data expertise. Work within multi-cloud environments — primarily AWS and Azure — anchoring data platform recommendations around Databricks where it fits the client's architecture and goals. Champion data governance through Unity Catalog — access control, lineage, data quality policies, and compliance — as a first-class part of every engagement, not an afterthought. Design data-to-application architectures — including Lakebase-backed services and Databricks Apps — that connect governed data to AI workflows, digital products, and user-facing experiences. Help build Livefront's Databricks practice — contributing to accelerators, internal enablement, certification goals, and Databricks partner go-to-market materials alongside delivery work. Why you should apply You want to work with passionate and talented people who are always looking for ways to make things better. You desire a work environment where respect, mutual trust, and egoless collaboration are paramount. You want colleagues who take their work seriously but not themselves, and who know how to let loose and have a good time. You like being part of a team with a reputation for excellence that gives back to the community by educating, mentoring, and sponsoring. You want to work on products and accounts that have outsized impact and reach. You believe in sweating the details, giving a damn about quality, and taking pride in going the extra mile. You want to help build a data practice specialization from the ground up — shaping how we go to market with Databricks, what we build as accelerators, and what it means to do this kind of work at a digital product company. What you bring to the table 5-8+ years of data engineering experience with at least 4 years in production Databricks environments, preferably in a consulting or client delivery context. Solid working knowledge of AWS and Azure cloud services relevant to Databricks deployments — storage, networking, IAM, and compute — with GCP familiarity a plus. Deep, production-grade Databricks expertise: Lakeflow Declarative Pipelines, Autoloader, Structured Streaming, Lakeflow Jobs, Unity Catalog (including fine-grained access control and lineage) — demonstrated through shipped production workloads, not prototypes. Proven experience designing Lakehouse architectures — medallion patterns, Delta Lake table design, partitioning, Z-ordering, and query optimization — at production scale. Hands-on experience with data pipeline testing, observability, and CI/CD for data — including unit testing, data quality frameworks, and version-controlled deployments via Git and Declarative Automation Bundles. Strong proficiency in SQL and Python, with the ability to write clean, performant, and maintainable code. Understanding of data modeling, schema design, and query optimization. Excellent communication skills with the ability to explain complex data concepts to both technical and non-technical stakeholders. Strong problem-solving skills with the ability to navigate ambiguous requirements and deliver pragmatic solutions. Above-average discipline and personal organization skills. Obvious comfort with critique and peer review in the context of an iterative development process. A demonstrated hunger for personal and professional growth. A self-evident love and care for the craft of data engineering. Bonus points if you… Have worked with real-time streaming technologies (Kafka, Kinesis, etc.). Have hands-on experience with alternative cloud data platforms — useful context for migrations and competitive assessments, though Databricks is our primary platform focus. Have experience in healthcare or fintech domains. Have hands-on experience with MLOps or LLMOps on Databricks — MLflow experiment tracking, model registry, Model Serving endpoints, or Vector Search for RAG pipelines. Have experience with Java, Go, or Scala. Have strong illustration skills for technical diagramming and data architecture documentation. Speak, write, and/or educate publicly about data engineering topics. Have contributed to open-source data projects. Hold or are actively pursuing a Databricks certification (Data Engineer Associate or Professional, or Apache Spark Developer) — we treat these as meaningful signals of platform depth, and they directly support our Databricks partner growth goals. Have experience with Databricks Apps, or Lakebase — early familiarity with where the Databricks platform is heading is a strong differentiator. What to expect When applying, please include a short note about yourself, a summary of your work experience, and a link to any public profiles you actively maintain (e.g., GitHub, LinkedIn, etc). Our hiring process moves quickly and consists of several stages for candidates who capture our attention with their initial submission, sometimes including but not limited to a short preliminary phone interview, a series of video interviews, and a short take-home exercise, which you'll have up to a week to complete. Additional information We go out of our way to evaluate all employees and job applicants equally based on merit, competence, and qualifications. We encourage candidates from all backgrounds to apply and consider all qualified applicants. Don't worry, every application will be reviewed by a human.

Location: United Kingdom, London Why Sony Interactive Entertainment? Sony Interactive Entertainment isn’t just the Best Place to Play — it’s also the Best Place to Work. Sony Interactive Entertainment (SIE) is the company behind the PlayStation brand. As a subsidiary of Sony Group Corporation, we’re part of a proud legacy of innovation and excellence. SIE is a dynamic technology company, delivering cutting-edge hardware and network services to more than 100 million people and an entertainment leader, home to some of the most beloved and recognizable intellectual properties (IP) in the world. Our role at SIE is to create and nurture the experiences under the PlayStation brand, a name synonymous with entertainment excellence and creativity. Department overview: At PlayStation, Data Science plays a critical role in shaping how we invest in, retain, and delight our global player base. The CLV team focuses on understanding player behaviour and driving more effective engagement across the player lifecycle — from acquisition and onboarding through to retention, monetisation, and long-term value. As a Data Scientist , you will develop models and insights that help drive more personalised player experiences and inform commercial and product decisions at scale. You will work in cross-functional teams to translate player behaviour into actionable strategies that drive measurable improvements in player engagement and value. This role is ideal for an individual who is comfortable owning problems end-to-end — from framing and modelling through to delivering impact — and partnering with stakeholders to support data-informed decision-making. What you’ll be doing: Own the development and delivery of machine learning models for use cases such as churn prediction, purchase propensity, store recommendations, customer lifetime value. Translate business problems into modelling approaches, selecting appropriate methods and features to deliver measurable impact. Work with large-scale behavioural and transactional data to uncover patterns and opportunities for player growth and engagement. Collaborate within cross-functional teams, including engineering, product, and commercial stakeholders, to ensure solutions are robust, scalable, and aligned with business needs. Partner with stakeholders across commercial, finance, and lifecycle teams to support decision-making with data-driven insights. Clearly communicate findings and recommendations to both technical and non-technical audiences. Develop and expand your understanding of more advanced modelling approaches (e.g. deep learning and sequence-based methods) as part of solving increasingly complex problems. What we are looking for: You're curious, analytical, and a strong problem-solver, with a structured approach to tackling business problems. You bring strong foundations in modelling and data manipulation, and are motivated by applying these to impactful, commercial problems. Experience building predictive models (e.g. churn, propensity, segmentation, or value modelling) in a commercial setting. Ability to independently take a problem from definition through to solution and delivery, demonstrating initiative and ownership. Proficiency in Python and SQL, and familiarity with common data science and ML libraries. Solid understanding of machine learning techniques (e.g. regression, tree-based models, clustering) and when to apply them, including how to refine and tune them for real-world problems. Strong communication and collaboration skills, with the ability to clearly articulate insights and work effectively with cross-functional stakeholders. Awareness of modern machine learning approaches (e.g. embeddings, sequence models, deep learning) and interest in applying them to real-world problems. Experience working with large datasets to generate actionable insights. A strong academic background, typically a Master’s or Ph.D. in a quantitative or technical field (e.g. Mathematics, Statistics, Computer Science). Nice to Have: Familiarity with production environments, MLOps, or data pipelines. Experience working with large-scale data using PySpark or equivalent distributed data processing tools. Experience in gaming, e-commerce, or subscription-based products. Benefits: Discretionary bonus opportunity Private Medical Insurance Dental Scheme 25 days holiday per year On Site Gym Subsidised Café Free soft drinks On site bar Access to cycle garage and showers Please refer to our Candidate Privacy Notice for more information about how we process your personal information, and your data protection rights. Please note, Sony Interactive Entertainment conducts background checks at the offer stage for all new employees (which may include criminal background checks for some roles) and will need to process personal information to support these checks. Please refer to our Candidate Privacy Notice (where applicable) for more information about what personal information we collect, how we use it, who we share it with, and your data protection rights. Equal Opportunity Statement: Sony is an Equal Opportunity Employer. All persons will receive consideration for employment without regard to gender (including gender identity, gender expression and gender reassignment), race (including colour, nationality, ethnic or national origin), religion or belief, marital or civil partnership status, disability, age, sexual orientation, pregnancy, maternity or parental status, trade union membership or membership in any other legally protected category. We strive to create an inclusive environment, empower employees and embrace diversity. We encourage everyone to respond. Sony Interactive Entertainment is a Fair Chance employer and qualified applicants with arrest and conviction records will be considered for employment.

Location: Remote Workplace: Remote ABOUT US Constructor is the next-generation platform for search and discovery in ecommerce, built to explicitly optimize for metrics like revenue, conversion rate, and profit. Our search engine is entirely invented in-house utilizing transformers and generative LLMs, and we use its core and personalization capabilities to power everything from search itself to recommendations to shopping agents. Engineering is by far our largest department, and we've built our proprietary engine to be the best on the market, having never lost an A/B test to a competitive technology. We're passionate about maintaining this and work on the bleeding edge of AI to do so. Out of necessity, our engine is built for extreme scale and powers over 1 billion queries every day across 150 languages and roughly 100 countries. It is used by some of the biggest ecommerce companies in the world like Sephora, Under Armour, and Petco. We're a passionate team who love solving problems and want to make our customers' and coworkers' lives better. We value empathy, openness, curiosity, continuous improvement, and are excited by metrics that matter. We believe that empowering everyone in a company to do what they do best can lead to great things. Constructor is a U.S. based company that has been in the market since 2019. It was founded by Eli Finkelshteyn and Dan McCormick who still lead the company today. ABOUT THE TEAM The Experiments team at Constructor builds an experimentation platform: traffic allocation, exposure logging, metric computation, statistical analysis, and a UI for launching experiments and interpreting results. It’s used both internally and by customers running experiments on production traffic. We’re scaling customer experimentation usage and investing in making experiments faster, more reliable, and easier to interpret. Unlike boxed experimentation tools, our platform has to support a wide range of customer integrations, traffic patterns, and industry constraints — which makes the engineering problems unusually real-world and high-leverage. Team: 3 Software Engineers, 1 Data Analyst, an Engineering Manager, and a Product Manager. THE ROLE This is primarily a backend role (Python services + data-heavy systems). You’ll occasionally work in the Dash UI and contribute to Spark jobs, but those are not the main focus. We care about correctness, observability, and product usability more than complex infrastructure. Tech: Python, FastAPI, PostgreSQL, Plotly Dash, PySpark/Databricks, AWS, CloudWatch, Sentry (Spark work is ~5–10% — we don’t expect you to be a data engineer or own the data platform.) WHAT YOU’LL DO - Build and maintain backend services for experiment assignment, logging, and report generation - Improve scalability, performance, and reliability of experiment reporting pipelines - Add product-facing features to the UI to help users launch experiments and interpret results - Take technical ownership of projects: shape solutions, break down work, and drive execution with the team - Participate in a weekly on-call rotation (investigating occasional issues and answering internal questions) You’ll collaborate closely with Search/Recommendations engineers, ML/DS partners, and customer-facing teams when needed. This role does not require frequent direct customer calls. REQUIREMENTS MUST-HAVE - Strong experience building and owning production backend services in Python - Experience with monitoring, alerting, and debugging user-facing systems - Strong SQL skills and confidence working with data-heavy systems (metrics, logging, analytics) - Ability to ship maintainable software (tests, code review, incremental delivery) MINDSET - You take ownership: you can turn ambiguous problems into an executable technical plan - You care about building systems people trust and rely on NICE-TO-HAVE - Experience with experimentation systems, metrics platforms, or A/B testing workflows - Experience with PySpark and/or Databricks - We don’t expect you to already be an expert in experimentation methodology — but we do expect strong backend fundamentals and the ability to ramp up effectively in a data-heavy domain LOCATION / TIME ZONES - We’re a distributed team. Preferred time zones are UTC+1 to UTC+7, but we’re flexible BENEFITS - 🏝️ Unlimited vacation time - we strongly encourage all employees to take at least 3 weeks per year - 🌎 Fully remote team - choose where you live - 🛋️ Work from home stipend - we want you to have the resources you need to set up your home office - 💻 Apple laptops provided for new employees - 🧑‍🎓 Training and development budget - refreshed each year for every employee - 👪 Maternity & Paternity leave for qualified employees - 🧠 Work with smart people who will help you grow and make a meaningful impact - 💵 Base salary: $80k–$110k USD, depending on knowledge, skills, experience, and interview results - 📈 Stock options - offered in addition to the base salary - 🎉 Regular team offsites to connect and collaborate DIVERSITY, EQUITY, AND INCLUSION AT CONSTRUCTOR At Constructor.io http://Constructor.io we are committed to cultivating a work environment that is diverse, equitable, and inclusive. As an equal opportunity employer, we welcome individuals of all backgrounds and provide equal opportunities to all applicants regardless of their education, diversity of opinion, race, color, religion, gender, gender expression, sexual orientation, national origin, genetics, disability, age, veteran status or affiliation in any other protected group. Studies have shown that women and people of color may be less likely to apply for jobs unless they meet every one of the qualifications listed. Our primary interest is in finding the best candidate for the job. We encourage you to apply even if you don’t meet all of our listed qualifications. How we handle your data. By submitting this application, you acknowledge that Constructor will collect and process the personal data you provide for the purpose of evaluating your candidacy for this role. If you are not selected, we will retain your data for a limited period as described in our Recruitment Privacy Notice https://constructor.com/recruitment-privacy-notice. After that period your data will be deleted or anonymized.

Location: Gibraltar; Malta; Georgia; Isle of Man; Poland; Romania; Ukraine Workplace: Remote About Patrianna Patrianna is a fast-scaling product development company headquartered in Gibraltar, with a dynamic, global team powering our growth. We operate at the intersection of technology and entertainment, building innovative solutions that shape the future of social gaming and deliver outstanding experiences to millions of players worldwide. We're driven by speed, ambition, and bold ideas. At our core, we're product creators and problem-solvers who thrive in a high-performance environment. We're looking for exceptional talent—smart, adaptable, and motivated individuals eager to make an impact, scale business functions at pace, and continuously improve. The Role Design, build, and manage advanced data systems that turn raw player data into actionable insights, powering personalized gaming experiences for millions. What you will be doing • Develop batch and real-time data pipelines on our GCP platform • Build APIs to share data with other teams and develop CI/CD pipelines for automated deployments • Develop Terraform IAC for deploying core infrastructure and use Datastream to ingest CDC data • Use Dataform and dbt to build medallion data architecture while optimizing BigQuery and ClickHouse models • Build Airflow DAG pipelines for data imports/exports with monitoring and alerting for data integrity • Uphold GDPR/CCPA compliance and maintain data governance standards across platforms What we are looking for • Strong proficiency in Bash, Git, SQL and Python (essential skills) • Hands-on experience with Terraform IAC, Docker and Kubernetes • Solid GCP expertise including BigQuery, Dataform, Airflow, and Datastream • Real-time processing experience with Kafka, PySpark, or Flink • Proven ETL/CI-CD development skills and API development capabilities • Bachelor's in Computer Science, Data Science, Information Systems, or equivalent experience Why you will love it Your pipelines will personalize gaming experiences for millions while detecting fraud and driving monetization. You'll work with cutting-edge GCP technology, enjoy autonomy in your role, and learn from a collaborative global team. We offer flexible remote options, professional development opportunities, and a culture that celebrates diverse ideas and breakthrough thinking. Equal Opportunities We hire based on skills, drive, and ideas—nothing else. Your background, gender, age, race, ethnicity, disability, sexual orientation, religion, neurodiversity, or educational path will never be a barrier to joining us. We also welcome candidates from non-traditional career journeys and value diverse perspectives that challenge conventional thinking. Diversity fuels our innovation, collaboration, and growth, and we're committed to creating an environment where everyone can contribute their best work and thrive.

Location: Dhahran, Saudi Arabia Muller's Solutions is seeking an experienced Data Engineer with 5–6 years of experience in designing, developing, and maintaining scalable data solutions. The ideal candidate should possess strong expertise in Python, Pandas, NumPy, Airflow, BigQuery, SQL, and API development . The candidate will play a key role in building robust data pipelines, processing large datasets, and supporting business-critical applications related to inventory optimization, master data management, and backend services. Key Responsibilities Design, develop, and maintain scalable ETL/ELT pipelines for data ingestion and processing. Build and optimize data workflows using Apache Airflow . Develop and maintain data processing solutions using Python , Pandas , and NumPy . Design and optimize complex SQL queries and data models in BigQuery . Develop and integrate APIs to support data exchange between systems. Implement data ingestion frameworks from various internal and external sources. Perform data cleansing, transformation, and validation to ensure data accuracy and consistency. Develop and maintain processes for master data management . Support inventory optimization initiatives by implementing data processing and business logic. Build and maintain backend services that enable data-driven applications. Monitor and troubleshoot data pipelines to ensure reliability and performance. Collaborate with cross-functional teams, including Product, Analytics, and Engineering teams. Ensure adherence to data governance, security, and best practices. Requirements Technical Requirements Must-Have Skills 5–6 years of experience in Data Engineering or related roles. Strong hands-on experience with: Python Pandas NumPy Apache Airflow Google BigQuery SQL API development and integration Experience in designing and implementing scalable ETL/ELT pipelines . Strong understanding of data modeling, data transformation, and database concepts. Experience working with large-scale datasets and optimizing query performance. Proficiency with version control systems such as Git . Good-to-Have Skills Experience with PySpark and distributed data processing. Basic understanding of Machine Learning concepts and workflows . Familiarity with data quality frameworks and data validation practices. Experience with Docker and containerization technologies. Knowledge of CI/CD pipelines and DevOps practices. Experience working in cloud-based data environments. Preferred Qualifications Bachelor's degree in Computer Science, Information Technology, Data Science, or a related field. Strong analytical and problem-solving skills. Excellent communication and collaboration abilities. Experience working on enterprise-scale data platforms and data-driven applications.

Location: Tallinn, Estonia Are you looking for the next professional opportunity that will challenge you and advance your career? Join our team now! Tickmill is looking to hire a Senior AI/ML Engineer (AI Lead) to join our rapidly expanding team. The ideal candidate will be a highly hands-on and business-oriented professional, capable of designing and delivering production-grade AI systems that drive measurable business impact. About Tickmill. Tickmill is an award-winning, multi-regulated broker offering access to a broad range of asset classes, including CFDs on Forex, Stocks, Indices, Commodities, Cryptocurrencies, and Bonds, as well as Exchange Traded Derivatives like Futures & Options). Founded in 2014, the Tickmill Group employs over 330 professionals across offices in London, Cyprus, Poland, Estonia, Seychelles and several other locations worldwide. Our culture is built on trust, transparency, and high standards.  We bring together ambitious professionals who are looking for an environment that supports them to dominate in their fields. Our diverse, multilingual teams are focused on innovation, delivering bold excellence, and raising the bar wherever we can. We offer competitive benefits packages, frequent team initiatives, and opportunities for professional growth. Join the Tigers! What does the role look like? The Senior AI/ML Engineer (Applied AI Lead) will have the chance to: • Design, develop, and productionise machine learning models end-to-end (training, validation, deployment, monitoring, retraining), ensuring reliability in real-world environments. • Lead the development of AI use cases such as client lifetime value (CLV), churn prediction, and fraud/abuse detection, with clear alignment to business outcomes and measurable impact. • Build and establish robust MLOps practices, including model deployment pipelines, CI/CD, environment promotion (dev/stage/prod), and lifecycle management. • Implement model monitoring frameworks to track performance, data drift, data quality, and business impact, with clear retraining and escalation strategies. • Ensure model explainability and transparency using techniques such as SHAP, feature attribution, and other interpretability methods appropriate for business-critical and regulated contexts. • Define and enforce best practices around model governance, documentation, versioning, and auditability, proportional to model risk and business impact. • Collaborate closely with Data Engineering to ensure high-quality data pipelines, feature engineering, reproducibility, and scalable data foundations. • Work cross-functionally with Product, Risk, Commercial, and other stakeholders to translate business problems into pragmatic AI solutions, balancing speed and robustness. • Drive continuous improvement through feedback loops, monitoring insights, and model retraining strategies, rather than one-off model delivery. • Mentor team members and promote best practices in production AI, MLOps, and applied machine learning delivery. What do you need to succeed in this role? • 5–8+ years of experience building and deploying machine learning models in production environments (not just experimentation). • Strong Python programming skills and solid software engineering fundamentals (testing, code quality, modular design, maintainability). • Strong understanding of machine learning concepts, model evaluation, feature engineering, and practical considerations in production systems (e.g. data leakage, drift, stability). • Hands-on experience with large-scale data processing (Spark / PySpark). • Experience with ML lifecycle tools (MLflow or similar) for experiment tracking, model management, and reproducibility. • Experience building and maintaining CI/CD pipelines (GitHub Actions preferred) for ML or data workflows. • Strong SQL skills and experience working with large, complex datasets in real-world environments. • Proven ability to deliver AI/ML solutions with measurable business impact, not just model performance improvements. • Experience working with model deployment, monitoring, drift detection, and retraining strategies in production systems. • Strong communication skills with the ability to work effectively with both technical and non-technical stakeholders, translating trade-offs clearly. • Ability to operate effectively in environments with evolving requirements, imperfect data, and delivery pressure, balancing MVP speed with production robustness. The below are considered as a plus: • Experience in fintech, trading, or financial services environments, particularly where models influence business-critical decisions. • Experience with real-time or streaming ML systems. • Familiarity with modern AI approaches such as LLMs, embeddings, or retrieval-augmented generation (RAG), particularly where applied to business workflows or integrated with structured data. • Experience working in regulated environments and implementing model governance frameworks (e.g. auditability, explainability, approvals, documentation standards). • Experience contributing to team standards, mentoring, or leading applied AI delivery. By joining us, you can expect: A Unique Opportunity for a career in a global, fast-growing company. Attractive remuneration package based on qualifications and experience. Opportunities to learn and grow through our “ Employee Training & Development program”. Stebby Sports Compensation or Confido Health Compensation. Multiple events to bond with the team and the group. Birthday and Loyalty benefits . What to expect from our recruitment process: Interview with HR Assessment task Interview with hiring managers Final interview with top management Make your next Career step and apply NOW! *Due to the great number of applications, we receive for each of our open vacancies, we are unable to respond on an individual basis.

Location: Limassol, Cyprus Are you looking for the next professional opportunity that will challenge you and advance your career? Join our team now! Tickmill is looking to hire a Senior AI/ML Engineer (AI Lead) to join our rapidly expanding team. The ideal candidate will be a highly hands-on and business-oriented professional, capable of designing and delivering production-grade AI systems that drive measurable business impact. About Tickmill. Tickmill is an award-winning, multi-regulated broker offering access to a broad range of asset classes, including CFDs on Forex, Stocks, Indices, Commodities, Cryptocurrencies, and Bonds, as well as Exchange Traded Derivatives like Futures & Options). Founded in 2014, the Tickmill Group employs over 330 professionals across offices in London, Cyprus, Poland, Estonia, Seychelles and several other locations worldwide. Our culture is built on trust, transparency, and high standards.  We bring together ambitious professionals who are looking for an environment that supports them to dominate in their fields. Our diverse, multilingual teams are focused on innovation, delivering bold excellence, and raising the bar wherever we can. We offer competitive benefits packages, frequent team initiatives, and opportunities for professional growth. Join the Tigers! What does the role look like? The Senior AI/ML Engineer (AI Lead) will have the chance to: • Design, develop, and productionise machine learning models end-to-end (training, validation, deployment, monitoring, retraining), ensuring reliability in real-world environments. • Lead the development of AI use cases such as client lifetime value (CLV), churn prediction, and fraud/abuse detection, with clear alignment to business outcomes and measurable impact. • Build and establish robust MLOps practices, including model deployment pipelines, CI/CD, environment promotion (dev/stage/prod), and lifecycle management. • Implement model monitoring frameworks to track performance, data drift, data quality, and business impact, with clear retraining and escalation strategies. • Ensure model explainability and transparency using techniques such as SHAP, feature attribution, and other interpretability methods appropriate for business-critical and regulated contexts. • Define and enforce best practices around model governance, documentation, versioning, and auditability, proportional to model risk and business impact. • Collaborate closely with Data Engineering to ensure high-quality data pipelines, feature engineering, reproducibility, and scalable data foundations. • Work cross-functionally with Product, Risk, Commercial, and other stakeholders to translate business problems into pragmatic AI solutions, balancing speed and robustness. • Drive continuous improvement through feedback loops, monitoring insights, and model retraining strategies, rather than one-off model delivery. • Mentor team members and promote best practices in production AI, MLOps, and applied machine learning delivery. What do you need to succeed in this role? • 5–8+ years of experience building and deploying machine learning models in production environments (not just experimentation). • Strong Python programming skills and solid software engineering fundamentals (testing, code quality, modular design, maintainability). • Strong understanding of machine learning concepts, model evaluation, feature engineering, and practical considerations in production systems (e.g. data leakage, drift, stability). • Hands-on experience with large-scale data processing (Spark / PySpark). • Experience with ML lifecycle tools (MLflow or similar) for experiment tracking, model management, and reproducibility. • Experience building and maintaining CI/CD pipelines (GitHub Actions preferred) for ML or data workflows. • Strong SQL skills and experience working with large, complex datasets in real-world environments. • Proven ability to deliver AI/ML solutions with measurable business impact, not just model performance improvements. • Experience working with model deployment, monitoring, drift detection, and retraining strategies in production systems. • Strong communication skills with the ability to work effectively with both technical and non-technical stakeholders, translating trade-offs clearly. • Ability to operate effectively in environments with evolving requirements, imperfect data, and delivery pressure, balancing MVP speed with production robustness. The below are considered as a plus: • Experience in fintech, trading, or financial services environments, particularly where models influence business-critical decisions. • Experience with real-time or streaming ML systems. • Familiarity with modern AI approaches such as LLMs, embeddings, or retrieval-augmented generation (RAG), particularly where applied to business workflows or integrated with structured data. • Experience working in regulated environments and implementing model governance frameworks (e.g. auditability, explainability, approvals, documentation standards). • Experience contributing to team standards, mentoring, or leading applied AI delivery. By joining us, you can expect: A Unique Opportunity for a career in a global, fast-growing company. Attractive remuneration package based on qualifications and experience (including 13th salary and Discretionary Bonuses to reward exceptional performance). Opportunities to learn and grow through our “ Employee Training & Development program”. Medical Insurance Cover , which includes Outpatient, Inpatient, and Dental Care. Multiple events to bond with the team and the group through Quarterly/Semestrial Team Activities for all the Company. Participation in our welfare investment and savings plan through our Provident Fund Scheme. Birthday and Loyalty benefits . Collaboration with SportBenefit. What to expect from our recruitment process: First interview with hiring managers or an HR call Task Assessment Technical Interview or home assignment Final interview with top management Make your next Career step and apply NOW! * Due to the great number of applications, we receive for each of our open vacancies, we are unable to respond on an individual basis.

Location: Chicago, IL, USA; New York, NY, USA; San Francisco, CA, USA About the role Chime's Data Science & Machine Learning team builds the models, services, and platforms behind how millions of members manage and grow their financial lives. We're hiring AI/ML Engineers across several teams, Trust & Safety, Lending, Growth, and Foundation Models, and you'll be matched with the team where your background, experience and interests fit best. In this role, you'll build and deploy machine learning systems on some of the richest transactional and behavioral data in fintech, turning it into decisions that protect members from fraud, expand access to credit, and power the personalized experiences and marketing that help millions of members get more out of products like MyPay, Instant Loans, and SpotMe — along with the foundational models the rest of our teams build on. This is a highly applied role: you'll own problems end to end, from framing the question through to a model running in production and moving a metric that matters. In this role, you can expect to Build, train, and deploy deep learning and classical ML models on large-scale financial, transactional, and behavioral datasets Take models from problem framing through to production — training, evaluation, deployment, monitoring, and iteration — and stay accountable for how they behave once they're live Design and improve the systems around the model: feature pipelines, batch and real-time inference, monitoring, and retraining Partner with Product, Engineering, Analytics, and Risk to turn ambiguous business problems into ML solutions, and to make sure the solution is the right one Connect model performance to member outcomes and business metrics, and use experimentation to prove impact Contribute to the shared ML platform, tooling, and standards that the rest of the team builds on Help identify where AI/ML creates measurable impact for members — and where a simpler answer is the better one To thrive in this role, you have Experience building and deploying deep learning models in production, with a solid grasp of architecture choice, training dynamics, and evaluation — and the judgment to model design choices Solid machine learning fundamentals: classical modeling, evaluation design, and knowing which metric actually answers the question in front of you Hands-on experience across the end-to-end ML lifecycle — training, experimentation, optimization, deployment, and monitoring Comfort with messy real-world data, including label definition, leakage, class imbalance, and train/serve skew Strong proficiency in Python and SQL, with deep learning frameworks such as PyTorch and distributed compute such as Spark or PySpark Working knowledge of modern ML infrastructure — AWS and tools such as SageMaker, Airflow, Kafka, Redis, and Snowflake — and an MLOps mindset for keeping production systems healthy The ability to operate independently in ambiguous environments, and to communicate clearly with both technical and non-technical partners Nice-to-Have Experience in any one of these is a plus, and helps us match you to the right team. Trust & Safety — fraud, risk, abuse detection, or adversarial modeling Lending — credit or underwriting models, and familiarity with model governance, explainability, and fairness requirements Growth — personalization, recommendation, marketing measurement, or experimentation at scale Foundation Models — transformers on tabular or semi-structured data, large-scale pretraining, ML platform work, or applied research with a publication record #LI-DA1 #LI-Onsite Below is the base salary offered for this role and level of experience. Full-time employees may also be eligible for bonus(es), competitive equity, and benefits. For commissioned roles, the base salary listed in this job description does not include incentive/variable pay. The actual base salary offered may be higher, depending on your location, skills, qualifications, and experience. Wage Notice $125,000 — $292,000 USD A little about us At Chime, we believe that everyone can achieve financial progress. We created Chime—a financial technology company, not a bank*—on the premise that core banking services should be helpful, easy, and free. Through our user-friendly tools and intuitive platforms, we empower our members to take control of their finances and work towards their goals. Whether it's starting a savings account, purchasing a first car or home, launching a business, or pursuing higher education, we're proud to have helped millions unlock their financial potential. We're a team of problem solvers, dreamers, and builders with one shared obsession: our members. From day one, Chimers have worked tirelessly to out-hustle and out-execute competitors to bring our mission to life. Their grit and determination inspire us to work harder every day to deliver the very best experience possible. We each bring an owner's mindset to our work, refusing to be outdone and holding ourselves accountable to meet and exceed the highest bars for our teams, our company, and our members. We believe in being bold, dreaming big, and taking risks, while also working together, embracing our diverse perspectives, and giving each other honest feedback. Our culture remains deeply entrepreneurial, encouraging every Chimer to see themselves as stewards of our mission to help everyday Americans unlock their financial progress. We know that to achieve our mission, we must earn and keep people's trust—so we hold ourselves to the highest standards of integrity in everything we do. These aren't just words on a wall—our values are embedded in every aspect of our business, serving as a north star that guides us as we work to help millions achieve their financial potential. Because if we don't—who will? *Chime is a financial technology company, not a bank. Banking services provided by The Bancorp Bank, N.A. or Stride Bank, N.A., Members FDIC. What we offer for our full-time, regular employees 🏢 Our in-office work policy is designed to keep you connected - with four days a week in the office and Fridays from home for those near one of our offices, plus team and company-wide events depending on location. 💻 Benefits that support your work and life, including backup child, elder, and pet care and subsidized commuter benefits for eligible employees. ✨ Comprehensive health, financial, and wellbeing benefits designed to support you at every stage of life. 🏝 Generous vacation policy and company-wide paid days off 🫂 1% of your time off to support local community organizations of your choice 👟 Annual wellness stipend to use towards eligible wellness related expenses 👶 Up to 22 weeks of paid parental leave for birthing parents and 12 weeks of paid parental leave for non-birthing parents 👪 Access to family planning reimbursement 💚 A challenging and fulfilling opportunity to join one of the most experienced teams in FinTech and help millions unlock financial progress We know that great work can’t be done without a diverse team and inclusive environment. That’s why we specifically look for individuals of varying strengths, skills, backgrounds, and ideas to join our team. We believe this gives us a competitive advantage to better serve our members and helps us all grow as Chimers and individuals. Chime is proud to be an Equal Opportunity Employer. We consider qualified applicants without regard to race, color, ancestry, religion, sex, national origin, sexual orientation, gender identity, age, marital or family status, disability, genetic information, veteran status, or any other legally protected basis under provincial, federal, state, and local laws, regulations, or ordinances. We will also consider qualified applicants with criminal histories in a manner consistent with the requirements of state and local laws, including the San Francisco Fair Chance Ordinance, Cook County Ordinance, NYC Fair Chance Act, and the LA City Fair Chance Ordinance, and consistent with Canadian provincial and federal laws. If you have a disability or special need that requires accommodation during any stage of the application process, please contact: accommodations@chime.com . To learn more about how Chime collects and uses your personal information during the application process, please see the Chime Applicant Privacy Notice .

Обязанности: • разработка и сопровождение AI-агентов для автоматизации процессов оценки кредитного риска • интеграция LLM и ML-моделей в агентные пайплайны (оркестрация, tool-calling, memory, RAG) • проектирование агентных архитектур: определение инструментов, стратегий и логики принятия решений • написание технической документации: описание агентов, регламенты, схемы взаимодействия. Требования: • опыт разработки на Python от 3 лет, особенно в контексте написания бизнес-логики или скриптов автоматизации • уверенное и архитектурных паттернов • опыт работы с агентными фреймворками: LangChain, LangGraph, AutoGen, CrewAI или аналоги • понимание ML (train/inference): xgboost, catboost, lightgbm, pytorch — опыт интеграции моделей в продуктовые системы • работа с Python-библиотеками pyspark / pandas / numpy для анализа данных • опыт работы с HTTP(S), RESTful API (интеграция агентов и моделей с внешними сервисами) • опыт работы с Docker; базовые знания Kubernetes и Helm • опыт работы с Git Условия: • комфортный современный офис рядом с м. Кутузовская • график работы- гибрид • ежегодный пересмотр зарплаты, годовая премия • корпоративный спортзал и зоны отдыха • более 400 образовательных программ СберУниверситета для профессионального и карьерного развития • расширенный ДМС, льготное страхование для семьи и корпоративная пенсионная программа • гибкий дисконт по ипотечному кредиту, равный 1/3 ключевой ставки ЦБ • бесплатная подписка СберПрайм+, скидки на продукты компаний-партнеров • вознаграждение за рекомендацию друзей в команду Сбера.

Location: Warsaw, Poland Are you looking for the next professional opportunity that will challenge you and advance your career? Join our team now! Tickmill is looking to hire a Senior AI/ML Engineer (AI Lead) to join our rapidly expanding team. The ideal candidate will be a highly hands-on and business-oriented professional, capable of designing and delivering production-grade AI systems that drive measurable business impact. About Tickmill. Tickmill is an award-winning, multi-regulated broker offering access to a broad range of asset classes, including CFDs on Forex, Stocks, Indices, Commodities, Cryptocurrencies, and Bonds, as well as Exchange Traded Derivatives like Futures & Options). Founded in 2014, the Tickmill Group employs over 330 professionals across offices in London, Cyprus, Poland, Estonia, Seychelles and several other locations worldwide. Our culture is built on trust, transparency, and high standards.  We bring together ambitious professionals who are looking for an environment that supports them to dominate in their fields. Our diverse, multilingual teams are focused on innovation, delivering bold excellence, and raising the bar wherever we can. We offer competitive benefits packages, frequent team initiatives, and opportunities for professional growth. Join the Tigers! What does the role look like? The Senior AI/ML Engineer (AI Lead) will have the chance to: • Design, develop, and productionise machine learning models end-to-end (training, validation, deployment, monitoring, retraining), ensuring reliability in real-world environments. • Lead the development of AI use cases such as client lifetime value (CLV), churn prediction, and fraud/abuse detection, with clear alignment to business outcomes and measurable impact. • Build and establish robust MLOps practices, including model deployment pipelines, CI/CD, environment promotion (dev/stage/prod), and lifecycle management. • Implement model monitoring frameworks to track performance, data drift, data quality, and business impact, with clear retraining and escalation strategies. • Ensure model explainability and transparency using techniques such as SHAP, feature attribution, and other interpretability methods appropriate for business-critical and regulated contexts. • Define and enforce best practices around model governance, documentation, versioning, and auditability, proportional to model risk and business impact. • Collaborate closely with Data Engineering to ensure high-quality data pipelines, feature engineering, reproducibility, and scalable data foundations. • Work cross-functionally with Product, Risk, Commercial, and other stakeholders to translate business problems into pragmatic AI solutions, balancing speed and robustness. • Drive continuous improvement through feedback loops, monitoring insights, and model retraining strategies, rather than one-off model delivery. • Mentor team members and promote best practices in production AI, MLOps, and applied machine learning delivery. What do you need to succeed in this role? • 5–8+ years of experience building and deploying machine learning models in production environments (not just experimentation). • Strong Python programming skills and solid software engineering fundamentals (testing, code quality, modular design, maintainability). • Strong understanding of machine learning concepts, model evaluation, feature engineering, and practical considerations in production systems (e.g. data leakage, drift, stability). • Hands-on experience with large-scale data processing (Spark / PySpark). • Experience with ML lifecycle tools (MLflow or similar) for experiment tracking, model management, and reproducibility. • Experience building and maintaining CI/CD pipelines (GitHub Actions preferred) for ML or data workflows. • Strong SQL skills and experience working with large, complex datasets in real-world environments. • Proven ability to deliver AI/ML solutions with measurable business impact, not just model performance improvements. • Experience working with model deployment, monitoring, drift detection, and retraining strategies in production systems. • Strong communication skills with the ability to work effectively with both technical and non-technical stakeholders, translating trade-offs clearly. • Ability to operate effectively in environments with evolving requirements, imperfect data, and delivery pressure, balancing MVP speed with production robustness. The below are considered as a plus: • Experience in fintech, trading, or financial services environments, particularly where models influence business-critical decisions. • Experience with real-time or streaming ML systems. • Familiarity with modern AI approaches such as LLMs, embeddings, or retrieval-augmented generation (RAG), particularly where applied to business workflows or integrated with structured data. • Experience working in regulated environments and implementing model governance frameworks (e.g. auditability, explainability, approvals, documentation standards). • Experience contributing to team standards, mentoring, or leading applied AI delivery. By joining us, you can expect: • Unique opportunity for a career in a global, fast-growing company. • Attractive remuneration package based on qualifications and experience. • Opportunities to learn and grow through our “Employee Training & Development program”. • Hybrid work flexibility. • Multiple events to bond with the team and the group. • Birthday and loyalty benefits. Make your next career step and apply NOW! *Due to the great number of applications, we receive for each of our open vacancies, we are unable to respond on an individual basis.

Задачи: — Управление проектами по внедрению Enterprise Data Warehouse (EDW) в банковской или финансовой сфере. — Управление Agile-процессами: проведение церемоний, планирование, отслеживание зависимостей. — Управление бюджетом и заинтересованными сторонами. Требования: — Подтверждённый опыт полного цикла внедрения проектов EDW в банковской или финансовой сфере. — Опыт управления Agile-проектами. — Понимание жизненного цикла управления данными и концепций Data Warehouse. — Опыт работы с PySpark или Informatica BDM, Cloudera Data Platform (CDP), Tableau или Power BI. — Свободное владение английским языком. Плюсом будет: — Опыт в AI/ML, Data Science или продвинутой аналитике. — Знание моделей данных в области риска, финансов, розничного или корпоративного банкинга. — Опыт работы с Python. — Опыт инициатив по корпоративному управлению данными. Условия: — Конкурентоспособный компенсационный пакет. — Поддержка релокации в Дубай, включая помощь с расходами на переезд. — Полное медицинское страхование. — Динамичная, международная рабочая среда.

Workplace: remote Location : Quantori is an international team: we have colleagues who work not only from office but also remotely from all over the world. Responsibilities : Design, build, and maintain scalable data pipelines for acquiring, integrating, and managing data from diverse data generation sources and systems (e.g., lab systems, MES, clinical supply, quality systems, external partners). Create and optimize data flows for structured and unstructured data using Python (PySpark), R, SQL, Databricks, Snowflake, and other modern engineering tools. Develop and maintain specific data repositories, implementing enterprise‑level data models, and creating new models as needed. Enable AI/ML readiness by ensuring data is well‑structured, versioned, traceable, and semantically aligned with enterprise data standards. What we expect : Bachelor’s degree in Engineering, Data Science, Life Sciences, Computer Science, or related field; advanced degree preferred. 6+ years of experience in data engineering, including data modeling and database design, preferably in a scientific, manufacturing, or healthcare environment. Proficiency with Python, R, SQL, and cloud-based architectures (AWS services, Snowflake, Databricks, Redshift) along with Spark and dbt. Familiarity with Databricks AI/BI or Tableau or other BI tools. Expertise in ETL and DWH. Experience with NoSQL and graph databases. English language proficiency of B2+ Strong analytical, problem‑solving, and stakeholder‑management skills, with the ability to translate discussions into actionable requirements. Ability to drive multiple exciting projects simultaneously with strong organizational skills and adaptability. Nice to have : Experience with regulated or standards‑driven data environments, such as CDISC, HL7, FHIR, OMOP, DICOM, or manufacturing/quality data standards. Familiarity with high‑dimensional data (e.g., imaging, sensor data, etc). Experience with principles connecting to or feeding MLOps and model deployment workflows. Knowledge of manufacturing systems (MES), laboratory information systems, or industrial data systems. Exposure to knowledge graph or ontology‑driven architectures. We offer : Competitive compensation Remote work Flexible working hours Continuous education, mentoring, and professional development programs A team with an excellent tech expertise

Location: Morocco We are looking to hire passionate Senior AI Engineers to help turn data into intelligent, production-ready solutions. You will work across the full AI stack: traditional machine-learning models, large language models (LLMs), computer-vision pipelines, and analytics / forecasting workflows. If you enjoy exploring data, building state-of-the-art models, and shipping reliable AI services, we would love to meet you. Responsibilities Model Development – Design, train, fine-tune, and evaluate models spanning classical ML, deep learning (CNNs, transformers), and generative AI (LLMs, diffusion). Data Exploration & Analytics – Conduct exploratory data analysis, statistical testing, and time-series / forecasting to inform features, prompts, and business KPIs. End-to-End Pipelines – Build reproducible workflows for data ingestion, feature engineering / prompt stores, training, CI/CD, and automated monitoring. LLM & Agentic AI Engineering – Craft prompts, retrieval-augmented generation (RAG) pipelines, and autonomous/assistive agents; fine-tune LLMs on domain-specific datasets to boost accuracy and align outputs with product requirements. AI Automation & Integration – Expose AI components as micro-services and event-driven workflows; integrate with orchestration tools (Airflow, Prefect) and business APIs to automate decision pipelines. Continuous Learning – Track advances in LLMs, vision, and analytics; share insights and best practices with the wider engineering team. Mentor junior engineers and contribute to technical direction and engineering best practices. Requirements BSc in Computer Science, Mathematics, or related field. 5+ years of professional experience working on AI/ML projects. Good command of English and Arabic (written and spoken). Proficient in Python and core libraries (PyTorch / TensorFlow, scikit-learn, pandas, NumPy). Solid understanding of machine-learning algorithms, deep-learning fundamentals, and basic statistics. Experience with data wrangling and visualization (Matplotlib / Plotly) and exploratory analysis. Familiarity with at least one of: OpenCV, Hugging Face Transformers, LangChain, MLflow, or similar. Good grasp of software-engineering best practices: Git, code reviews, testing, CI. Preferred Qualifications Knowledge of C++ or C# for performance-critical modules. Experience deploying models via Docker, Kubernetes, or cloud AI services. Exposure to vector databases and RAG workflows. Skill in BI / dashboard tools (Power BI, Tableau, Streamlit) or time-series frameworks (Prophet, statsmodels). Familiarity with MLOps / LLMOps tooling (DVC, MLflow Tracking, Weights & Biases, BentoML). Experience with image processing techniques (e.g., OpenCV, image segmentation, feature extraction) Experience with Spark (PySpark) and distributed data processing, including usage of platforms such as Databricks, AWS EMR, or GCP Dataproc. Strong SQL skills and experience working with large-scale datasets, including partitioning and performance tuning. Familiarity with modern data lake architectures and scalable data storage concepts.

Location: Москва О нас: Мы команда Цифрового Ассистента B2C. Мы создаем продукт, который ежедневно помогает десяткам миллионов наших клиентов на новом уровне пользоваться услугами и продуктами Банка и Экосистемы. Сейчас наша команда разрабатывает новый функционал, позволяющий клиентам решать не только повседневные, но и более сложные комплексные задачи и цели, которые могут состоять из множества взаимосвязанных шагов и быть распределенными во времени. Для решения задачи в нашем распоряжении цифровая платформа и широкий набор инструментов, представленных мультиагентной системой на основе LLM GigaChat, интегрированной со множеством банковских сервисов. Мы предлагаем: ·      Возможность создавать и развивать инновационный продукт, использующий передовые технологии и задающий тренды в отрасли. ·      Работу в кросс-функциональной команде сильных специалистов. ·      Широкое пространство для развития и реализации ваших идей и экспертизы. ·      Современный технологический стек и развитая IT-инфраструктура. Обязанности * Проектирование и разработка новых платформенных инструментов и агентов и расширение функционала существующих * Проведение глубокого анализа данных и пользовательских сценариев мультиагентной системы, проработка функциональных требований к новым специализированным агентам. * Разработка и улучшение платформенных механизмов, таких как система роутинга, оркестрации, планирования, управления контекстом и памятью, извлечения инсайтов. * Разработка комплексной системы метрик качества как отдельных компонентов системы, так целых компонентов системы. * Создание пайплайнов автоматизации мониторинга и визуализации метрик, в том числе с применением подхода LLM-as-a-Judge. Требования ·      Высшее техническое или математическое образование. ·      Опыт в сфере машинного обучения и анализа данных от трех лет. ·      Глубокое понимание принципов работы и архитектур LLM, NLP и классических ML-алгоритмов, знание математической статистики и теории вероятностей. ·      Владение на высоком уровне основными инструментами и фреймворками для ML разработки, аналитики и обработки больших данных, работы с LLM агентами (pandas, numpy, sklearn, pytorch, langchain, langgraph, pyspark) ·      Уверенные практические навыки работы с LLM и промпт-инжениринга. ·      Понимание принципов построения промышленных микросервисных архитектур, мультиагентных систем и LLM-приложений. Условия ·      Гибридный режим работы, комфортный современный офис на м. Кутузовская. ·      Ежегодный пересмотр зарплаты, годовая премия. ·      Корпоративный спортзал и зоны отдыха. ·      Более 400 образовательных программ СберУниверситета для профессионального и карьерного развития. ·      Расширенный ДМС, льготное страхование для семьи и корпоративная пенсионная программа. ·      Гибкий дисконт по ипотечному кредиту, равный 1/3 ключевой ставки ЦБ. ·      Бесплатная подписка СберПрайм+, скидки на продукты компаний-партнеров. ·      Вознаграждение за рекомендацию друзей в команду Сбера.

Location: Algeria (West); Algeria (East); Algiers, Algeria Workplace: hybrid Yassir is the leading super App in the Maghreb region set to changing the way daily services are provided. It currently operates in 45 cities across Algeria, Morocco and Tunisia with recent expansions into France, Canada and Sub-Saharan Africa. It is backed (~$200M in funding) by VCs from Silicon Valley, Europe and other parts of the world. We offer on-demand services such as ride-hailing and last-mile delivery. Building on this infrastructure, we are now introducing financial services to help our users pay, save and borrow digitally. Helping usher the continent into a digital economy era. We’re not just about serving people - we’re about creating a marketplace to bring people what they need while infusing social values. What you’ll do Build a centralized data lake on GCP Data services by integrating diverse data sources throughout the enterprise. Develop, maintain, and optimize SPARK-powered batch and streaming data processing pipelines. Leverage GCP data services for complex data engineering tasks and ensure smooth integration with other platform components Design and implement data validation and quality checks to ensure data's accuracy, completeness, and consistency as it flows through the pipelines. Work with the Data Science and Machine Learning teams to engage in advanced analytics. Collaborate with cross-functional teams, including data analysts, business users, and operational and marketing teams, to extract insights and value from data. Collaborate with the product team to design, implement, and maintain the data models for analytical use cases. Design, develop, and upkeep data dashboards for various teams using Looker Studio. Engage in technology explorations, research and development, POC’s, and conduct deep investigations and troubleshooting. Design and manage ETL/ELT processes, ensuring data integrity, availability, and performance. Troubleshoot data issues and conduct root cause analysis when reporting data is in question. Required Technical Skills PySpark GCP - Big Query, Dataproc, Dataflow, Dataplex, Pub-Sub and Cloud Storage Advanced SQL knowledge NoSQL (Preferably MongoDB) Programming languages - Scala/Python Great Expectation - similar DQ framework Familiarity with workflow management tools like Airflow, Prefect or Luigi. Understanding of Data Governance, DWH, and Data Modelling Good to have skills Infrastructure as Code - Terraform Docker and Kubernetes Looker Studio AI and ML engineering knowledge At Yassir, we believe in the power of diversity and the importance of an inclusive culture. So, if you're ready to bring your unique perspective and experiences to the table, then we're excited to listen. Don't just apply for a job, come and be a part of our journey. Let's create a better tomorrow together. We look forward to receiving your application! Best of luck, Your Yassir TA Team