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Responsibilities: • Conduct group sessions on AI, Data Science, ML models • Answer student questions and provide project feedback • Help find practical solutions beyond textbooks • Support students in their career transition • Participate in improving the curriculum Requirements: • Kazakh (C2) • three+ years experience in Data Science • Knowledge of mathematical statistics • Experience with regression and classification models • Familiarity with numpy, scikit-learn, pandas Optional requirements: • Experience in mentorship, teaching, or team leading • Experience in Computer Vision and RecSys projects • Participation in hackathons

Location: Paris Qube Research & Technologies (QRT) is a global quantitative and systematic investment manager, operating in all liquid asset classes across the world. We are a technology and data driven group implementing a scientific approach to investing. Combining data, research, technology and trading expertise has shaped QRT’s collaborative mindset which enables us to solve the most complex challenges. QRT’s culture of innovation continuously drives our ambition to deliver high quality returns for our investors. Your future role within QRT: As part of the Data Search & Analytics team, you will support one of QRT's Options trading streams, working closely with quantitative researchers and traders to source, curate and deliver high-quality data that powers systematic options research and trading. In addition to the core responsibilities outlined above, you will: Source, evaluate and onboard datasets relevant to options and derivatives research. Partner closely with researchers and traders to understand data requirements and translate them into scalable data solutions. Build and maintain robust data pipelines, ensuring the quality, consistency and timeliness of options-related datasets. Develop a strong understanding of options market data and contribute to improving QRT's internal data platform and tooling. Proactively identify opportunities to enhance data coverage, quality and operational efficiency for the Options business. Your present skillset: 3+ years of experience as a Data Scientist, Data Engineer or in a similar data-focused role; experience supporting quantitative research or trading teams is advantageous. Experience working with financial market data; exposure to options, derivatives or volatility data is a strong advantage. Strong Python programming skills, with experience using data processing libraries such as Pandas and NumPy. Experience building data pipelines and working with large-scale structured and time-series datasets. Demonstrated interest in financial markets and the application of data to quantitative research and trading. Excellent communication and stakeholder management skills, with the ability to collaborate effectively with researchers, traders, engineers and external data vendors. Ability to thrive in a fast-paced, high-performance environment while managing multiple data initiatives simultaneously. QRT is an equal opportunity employer. We welcome diversity as essential to our success. QRT empowers employees to work openly and respectfully to achieve collective success. In addition to professional achievement, we are offering initiatives and programs to enable employees achieve a healthy work-life balance.

Location: European Union Company Intro At Toloka AI we create data that powers leading GenAI models and innovations. We work with frontier labs, big tech, renowned AI startups, enterprises and non-profit research organizations worldwide. We use a combination of Experts + Crowd + Tech Platform to teach AI models to reason and evaluate their efficacy and safety. We have experts in more than 50 different domains—from doctors and lawyers to physicists and engineers—and boast one of the most diverse global crowds, representing over 100 countries and speaking 40+ languages . We are a well-funded startup with an enviable portfolio of clients including Anthropic , Amazon , Microsoft , Poolside , Recraft ,  and Shopify . Recently, we secured strategic investment led by Bezos Expeditions and Nebius Group with participation from Mikhail Parakhin , CTO of Shopify and board advisor to leading GenAI companies, who now serves as our Chairman of the Board. Our remote-first team is globally distributed around the world: USA , UK , the Netherlands , Serbia , and more. About position The Forward Deployed AI Engineer Engineer is Toloka's most senior client-facing technical role. You will partner directly with CTOs, VPs of Engineering and ML, research leaders, and applied AI teams, helping them solve complex AI data challenges and translate ambitious model goals into practical, scalable solutions. This is a hands-on role . You will design and build the data-generation , labeling , and evaluation pipelines t hat power the next generation of AI models . Rather than training the models yourself, you'll help clients identify the right data strategy and own the solution from problem discovery through implementation and delivery. Beyond delivering client solutions, you'll help shape how Toloka approaches AI solution engineering - establishing best practices, raising the technical bar across engagements, and mentoring Solution Engineers and Technical Consultants . Why this role is different Every engagement is different. One week you might be designing evaluation pipelines for frontier foundation models, the next helping an enterprise build domain-specific reasoning datasets or synthetic data workflows. This role sits at the intersection of consulting, engineering, and AI. You'll work directly with some of the world's leading AI companies, helping shape how next-generation models are trained and evaluated. What you'll do Executive partnership Act as the primary technical counterpart to CTOs, VPs of Engineering, and research/engineering leadership. Lead executive conversations using a structured, answer-first (BLUF) approach. Manage escalations and expectations with composure and integrity. Build long-term trusted relationships by recommending evidence-based solutions. Data strategy & needs diagnosis Ask excellent questions to uncover the client's real need - the "question behind the question." Understand their model, which metrics they want to move, and how they intend to train or evaluate it. Draw on a solid understanding of how LLMs are trained and fine-tuned to have credible conversations with their technical leaders, understand their data strategy, and proactively propose the data that will solve their problem - with options and rationale ("based on your goal, you likely need this, or this"). Solution engineering & delivery (hands-on) Design and build the data solutions yourself: configure data-labeling components and quality controls, develop user interfaces and AI-driven solutions (e.g. agentic systems, RAG, synthetic data generation), and integrate them into automated, multi-stage pipelines that produce data for AI training and evaluation. Architect and reason about complex, multi-stage solutions end to end; run experiments to prove the pipeline delivers data of the required quality and speed; iterate from MVP toward production. Provide technical leadership across multiple client engagements, establish reusable engineering standards and best practices, and mentor Solution Engineers and Technical Consultants to raise the overall technical bar of the organization. Commercial ownership Own delivery end to end - timelines, quality, and scalability - and the commercial outcome : Gross Margin and Contribution Margin, with the levers, trade-offs, and next checkpoints named, not just described. Identify and drive expansion opportunities. About You No one is expected to meet every requirement below. If you bring a strong combination of client-facing communication, AI/LLM expertise, and hands-on engineering experience, we'd love to hear from you. Executive communication. You can confidently lead conversations with CTOs, VPs, and senior technical stakeholders - clear, concise, structured, persuasive, and calm under pressure. Professional English (C1+) is essential. Strong understanding of modern LLM development. You understand how modern LLMs are trained, fine-tuned, and evaluated (including SFT, RLHF/RLAIF, DPO/PPO, reward modeling, and LoRA/PEFT). You're comfortable discussing these topics with senior technical stakeholders and translating their goals into effective AI data solutions. Hands-on solution engineering. You've designed complex AI solutions, built multi-stage pipelines, and developed AI-driven systems (e.g. agentic workflows, RAG, or synthetic data generation). You're comfortable working in Python (NumPy, Pandas), integrating APIs, and independently prototyping LLM-powered solutions. Solid software engineering foundations. You understand version control, testing, MVP thinking, and iterative development. Experience building production software is a strong plus. Exceptional discovery and problem framing. You enjoy working through ambiguity, asking the right questions, and translating business or research goals into clear AI data and evaluation strategies. Seniority and track record. 8+ years delivering complex AI, ML, or data projects end to end, with experience influencing technical direction beyond individual projects. Experience establishing engineering standards, mentoring engineers, or leading cross-functional technical initiatives is highly valued. Experience in top-tier strategy consulting (McKinsey, Bain, BCG) and/or as an applied ML / LLM engineer is a strong advantage. Ownership mindset. You take responsibility for outcomes, make thoughtful trade-offs between time, cost, and quality, and are comfortable owning both technical and commercial success. Nice to have Hands-on experience training or fine-tuning LLMs and/or building agentic systems (helps you reason about client needs - though the role itself is about building data solutions, not training models). Advanced degree (MSc/PhD) in AI, CS, or a related field. Experience in crowdsourcing and/or data-centric AI, and with fast-paced, multi-project delivery for frontier AI clients. What we can offer Freelance (B2B) collaboration , with a path into a project team if things go well; Flexible, fully remote schedule ( 40 hours per week ); Work at the leading edge of AI development, alongside a dedicated and dynamic team of experts; Projects with customers that are AI industry leaders and well-known household names; Friendly community. [Important Notice] Scam Alert Regarding Fake Job Postings It has come to our attention that an individual or group is fraudulently impersonating Toloka to post fake jobs and solicit personal information from applicants.Please be aware: Official Communication: Our recruiting team will only contact you from an official " toloka.ai " email address. We will NEVER use Gmail, Yahoo, Tolokainc, toloka.inc,  or other personal or seemingly business email accounts. Our Process: We will never ask for your bank account details, credit card number, or any fees as part of the application or interview process. Official Listings: All legitimate job openings are posted on our official careers page: https://toloka.ai/careers#job-list To learn how we collect, use, disclose, and store personal data, check out our Privacy Notice . What to do: If you see a suspicious job posting or have been contacted by someone you suspect is a scammer, please do not provide any personal information. Instead, report the incident to us directly at security@toloka.ai and report the profile/post to LinkedIn.We are taking this matter very seriously and are working with the appropriate parties to resolve it. Thank you for your vigilance! To learn how we collect, use, disclose, and store personal data, check out our Privacy Notice .

Location: Berlin Workplace: OnSite ABOUT PEEC AI AI search is changing how brands are discovered and chosen. People now ask ChatGPT, Gemini, Claude and Google’s AI answers what they used to search for, and AI assistants increasingly shape the journey from discovery to purchase. Winning customers now means winning over the AI systems that influence their decisions. Peec AI is an AI search platform that helps marketing teams understand and improve how their brands are discovered, represented, and recommended. We show them where they stand, what’s shaping it, and where to act. Since launching in January 2025, Peec AI has passed $20M ARR and 4,000 paying customers. We’re a Series A company backed by 20VC and Singular, with 100+ people across our Berlin HQ and our New York office. WHAT YOU’LL DO - Train, test, and ship models that power Peec AI’s recommendations - helping customers boost their visibility in AI search - Develop algorithms that extract actionable insights from AI search behavior, creating data-driven recommendations that help brands increase their AI Search visibility - Own the full model lifecycle from experimentation to production deployment, working closely with engineering to integrate ML solutions into our systems - Design and implement data pipelines to ingest, process, and analyze large volumes of data WHAT WE’RE LOOKING FOR - Proven backend development skills in Python with experience building APIs, data pipelines, or ML infrastructure, and familiarity with tools like FastAPI, Docker, and cloud platforms (preferably GCP) - Deep curiosity about how LLMs work, with the ability to reverse-engineer AI search behavior and translate patterns into actionable product features - Track record of taking projects from research to production in a fast-moving startup environment, with strong problem-solving skills and comfort working with ambiguous, evolving problems - Excellent communication skills with the ability to explain complex technical concepts to customers and stakeholders OUR DATA SCIENCE STACK - Languages: Python, SQL - Libraries: Pandas, NumPy, HuggingFace, PyTorch, TensorFlow, ONNX - Backend: GCP, Cloud Functions, Firestore, Postgres, AlloyDB, BigQuery - AI Models: OpenAI, Claude, Perplexity, Gemini, Llama, etc. BONUS POINTS - Contributions to open-source projects - Deployed side/hobby projects that we can check out - Presented research papers at top ML or AI conferences - Having started a company before or worked at a high-growth startup - Fluency in Typescript WHAT WE OFFER - Exciting and challenging work with real impact and ownership at one of Europe’s fastest-growing Series A startups - Regular team events and off-sites - Aggressive equity compensation package - Paid Dinner & Uber home when working late - The most beautiful office space and work environment in Berlin

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.

Задачи: — Лидировать в продукте направление системного анализа бизнес-требований при проектировании архитектуры и API (REST) для новых сервисов — Разрабатывать и поддерживать микросервисы на FastAPI и взаимодействовать со смежными командами для интеграции компонентов и ML-моделей в сервисы — Разработывать промпты и сценарии для генеративных моделей — Оптимизировать производительность сервисов на всех уровнях: код (asyncio, threading), хранение данных и запросы (продвинутый SQL, индексы, шардирование), кэширование Ignite, работа с очередями Kafka, создание и оптимизация RAG-систем для работы с данными — Участвовать в проектировании тестовых сценариев и создании тестовых датасетов, контролировать качество реализации, проводить глубокий анализ дефектов, определять их корневые причины. Требования: — Коммерческий опыт разработки на Python от 2 лет, включая самостоятельное проектирование архитектуры компонентов сервисов на основе бизнес-требований и использование фреймворков для ML — Практический опыт создания высоконагруженных и отказоустойчивых микросервисов на FastAPI, а также продуктов с внедрением ML решений — Знания в области асинхронных приложений, продвинутый SQL (оконные функции, оптимизация запросов), опыт с брокерами сообщений Kafka, с Docker и Kubernetes — Знания алгоритмов и структур данных — Понимание принципов работы генеративных моделей (GPT, GigaChat и др.) — Высокая культура совместной работы с BitBucket/Jira, бережное отношение к коммитам, знание процессов Agile (Scrum) — Опыт в системном/бизнес-анализе будет плюсом — Знание основ современных LLM, архитектуры AI-агентов и опыт интеграции ML-решений (NLP, LLM) в продукты будет плюсом — Навыки использования AI-инструментов (GigaChat, DeepSeek, ChatGPT) для анализа, генерации и автоматизации будут плюсом — Практический опыт работы с фреймворками ML/DS: (LangChain/GIgaChain, Scikit-Learn, Pandas и др.) будет плюсом. Условия: — Комфортный современный офис на м.Кутузовская — Формат работы офис на время адаптации, далее гибрид — Ежегодный пересмотр зарплаты и годовая премия — Корпоративный спортзал и зоны отдыха — Более 400 образовательных программ СберУниверситета для профессионального и карьерного развития — Расширенный ДМС, льготное страхование для семьи — 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.

Обязанности: • проведение углубленного анализа данных розничного кредитования • разработка, совершенствование и поддержка дашбордов и регулярной отчетности по ключевым метрикам риска • автоматизация рутинных процессов и аналитических задач • интерпретация результатов анализа для руководства и смежных подразделений, формулирование рекомендаций для управленческих решений Требования: • глубокие практические знания в области управления розничными кредитными рисками. Обязателен успешный опыт работы на всех этапах жизненного цикла кредита: от скоринга заявок до управления проблемной задолженностью и работой с дефолтами. • продвинутое владение SQL (написание и оптимизация сложных запросов, построение и поддержка структур данных для аналитики). • опыт применения Python для анализа данных (Pandas, NumPy, SciPy и т.д.). • навыки визуализации данных и презентации аналитических выводов. Способность ясно и структурированно доносить сложную информацию до различной аудитории, включая технических специалистов и топ-менеджмент • навыки работы с генеративными AI-моделями; опыт создания AI-агентов и использования их в работе будет преимущество • опыт использования GigaChat, Kandinsky и аналогов в продуктах, навыки создания и использования AI-агентов • инструментальное владение AI для анализа, генерации и автоматизации Условия: • комфортный современный офис • офисный формат работы • 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 • бесплатная подписка СберПрайм+, скидки на продукты компаний-партнеров • вознаграждение за рекомендацию друзей в команду Сбера.

Location: New York City, New York What you’ll be part of Garner is on a mission to transform the U.S. healthcare system — and we’re the only proven player doing exactly that. We partner with employers to redesign how healthcare works: applying 550+ proprietary clinical metrics across 80+ specialties to a dataset of 320M+ patients to identify the best-performing doctors, then using compelling incentives to steer members to the care that helps them get healthier, faster. The result is a rare “win win” — better care and lower costs for both members and employers. In just five years, our work has helped over 2.5 million people access higher-quality care and saved $1B in healthcare costs. We recently raised our Series E and have doubled five years running. If you've ever wanted your work to solve a problem that touches every person in this country, this is the opportunity to do exactly that. You'd be joining a team fundamentally reimagining healthcare in the U.S. — and using AI to scale that impact further and faster than anyone else can. About the role We are seeking an exceptional Staff Applied Researcher to join our Applied Science team. You will be responsible for building the algorithmic systems that power Garner — determining how we evaluate providers, make recommendations, and optimize for outcomes across cost, quality, and access. You will be responsible for turning ambiguous, real-world problems into systems that deliver measurable impact, defining the objective functions, metrics, and logic that drive our product. You will own these systems end-to-end, from problem definition through production and ongoing performance. Where you will work: This role will be based in our New York City office (in the Financial District). You must be willing to work in the office 3 days per week on Tuesday, Wednesday and Thursday. What you will do: Own the most ambiguous, high-stakes problems facing the company end-to-end, and set how the team frames and approaches them Frame messy, real-world healthcare and business constraints into clear objectives, tradeoffs, and decision frameworks Define the set of metrics needed to judge whether a solution is working, and validate solutions before they ship Choose the right approach for each problem, from machine learning to optimization to heuristics to simple rules, based on what the problem actually calls for, and set the standard for how the team selects and applies these approaches Deliver algorithmic breakthroughs that move the company's most important metrics, pioneering approaches that become how applied science is done at Garner Review applied science work at the highest level across the company, ensuring the methods used across teams are sound and correctly applied Build a deep understanding of the healthcare economy and Garner's place in it To make the role concrete, here are three problems on our near-term roadmap: Provider tiering optimization. Build a tiering algorithm that jointly optimizes geographic access and total-cost-of-care savings across our doctor network. The objective function, constraints, and tradeoff surface are all open design questions. AI primary care doctor. Fine-tune and productionize an LLM-based primary care experience on our website, including the evaluation harness, guardrails, and ongoing quality monitoring needed to ship a medical-adjacent product safely. Member engagement model. Build an ML system that ingests claims data and in-app behavior to choose the right channel and moment for each touchpoint — SMS, push, phone, or email — to influence member behavior toward better-quality, lower-cost care. The ideal candidate has: 6+ years of industry experience as an Applied Scientist, Machine Learning Engineer, Research Scientist, or equivalent; or 4+ years of industry experience with a relevant advanced degree, PhDs preferred A bias toward action, quickly translating ideas into working prototypes to test approaches Strong applied problem-solving skills, with the ability to define good metrics and then deliver solutions that improve them Recognized technical authority, with the judgment to ensure the techniques used across an organization are sound Strong judgment in choosing between statistical models, heuristics, optimization approaches, and simpler algorithmic methods depending on the problem Strong communication skills, including at the executive level, with a track record of driving alignment across an organization A desire to be a part of a high-performing, mission-driven team that operates with urgency, a strong sense of individual accountability, and a commitment to authentic feedback What you’ll get here You’ll work on problems that matter, at a company working to change healthcare at scale. You’ll work at the intersection of AI and systemic healthcare reform, where the problems we solve are as interesting and compelling as the mission. At Garner, you’ll take on real, ambitious problems with real ownership and autonomy, alongside exceptional, principles-based people who genuinely want you to win. It’s demanding by design. You’ll be challenged to stretch beyond what you thought possible and receive consistent coaching to help you grow and do the best work of your career. This isn't the right fit for everyone, and that's intentional. The people here are driven by what's at stake for real people, and that's what gives our intensity its purpose. Technologies we use Python, SQL, AWS, Snowflake, pandas, XGBoost, PyTorch, HuggingFace, modern LLM tooling and eval frameworks. We pick tools based on the problem, not the resume — bring your judgment. Why this role This is a unique opportunity to work on high-impact search problems in healthcare, helping shape how members find better care through algorithmic systems that directly influence healthcare outcomes. Compensation Transparency: The target base comp range for this position is $260,000 – $382,000 . Individual compensation for this role will depend on various factors, including qualifications, skills, and applicable laws. In addition to base compensation, this role is eligible to participate in our equity incentive and competitive benefits plans, including but not limited to: flexible PTO, Medical/Dental/Vision plan options, 401(k) with company match, flexible spending accounts, Teladoc Health and more. Fraud and Security Notice: Please be aware of recent job scam attempts. Our recruiters use getgarner.com and garnerhealth.com email domains exclusively. If you have been contacted by someone claiming to be a Garner recruiter or a hiring manager from a different domain about a potential job, please report it to law enforcement here and to candidateprotection@garnerhealth.com . Equal Employment Opportunity: Garner Health is proud to be an Equal Employment Opportunity employer and values diversity in the workplace. We do not discriminate based upon race, religion, color, national origin, sex (including pregnancy, childbirth, reproductive health decisions, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, genetic information, political views or activity, or other applicable legally protected characteristics. Garner Health is committed to providing accommodations for qualified individuals with disabilities in our recruiting process. If you need assistance or an accommodation due to a disability, you may contact us at talent@garnerhealth.com .

Задачи: — Генерировать и исследовать идеи торговых стратегий, выявлять источники доходности и неэффективности на DEX-perps, с приоритетом на рыночно-нейтральные конструкции. — Разрабатывать и тестировать стратегии на исторических данных с учетом комиссий, проскальзывания, funding rate и других факторов исполнения. — Оценивать стратегии по риск-доходности, анализировать Sharpe ratio, максимальную просадку, поведение в разных рыночных режимах и скрытые экспозиции. — Доводить торговую логику до кода, готового к исполнению, и пересматривать стратегии по мере изменения рыночной среды. — Документировать каждую стратегию: логику, параметры, ожидаемый профиль доходности, ограничения и условия отказа. — Взаимодействовать с аналитиками DeFi и внешними партнерами в рамках исследования и внедрения стратегий. Требования: — Опыт разработки, тестирования и валидации торговых стратегий, ориентированный на самостоятельную работу. — Глубокое понимание perpetual futures: funding, basis, margin, liquidation mechanics, order book dynamics. — Практический опыт работы с DEX-perps, например Hyperliquid, и их API. — Уверенная работа с Python и инструментами для анализа данных и временных рядов: pandas, Polars, NumPy. — Строить стратегии с учетом risk management с самого начала. — Подтвержденный опыт live-стратегий и реальным PnL. — Опыт с направленными, статистическими и событийными стратегиями будет преимуществом. — Опыт работы с CEX API будет плюсом. Условия: — Конкурентная заработная плата, обсуждаемая по итогам интервью. — Оформление по ТК Кипра. — Бесплатные завтраки и обеды в офисе. — Компенсация абонемента в спортзал. — Компенсация 50% стоимости курсов английского языка. — График работы 5/2 с гибким началом рабочего дня с 8:00 до 10:00. — Возможность принимать самостоятельные решения и видеть результат своей работы в цифрах. — Юридическое сопровождение процесса переезда.

Location: Worldwide We are looking for a Data Scientist for our Integrated Risk Management team to help develop risk models and analytical frameworks that support key business decisions across credit, financial, and operational risk domains. In this role, you will work on complex quantitative challenges, build predictive and diagnostic models, and partner with stakeholders across the organization to improve risk visibility, portfolio performance, and decision-making. Challenges that await you: Develop and implement advanced statistical models for our credit portfolio at both per-account and aggregate levels to predict behavior and optimize performance Create sophisticated Net Present Value (NPV) models and their core components on a per-account basis for our various credit products Model critical financial metrics, including FX position, liquidity, reserves, and other balance sheet items to support robust financial management Analyze and model not only expected outcomes but also their deviations, distributions, and uncertainty, recognizing the inherently probabilistic nature of financial and risk data Design diagnostic and forecasting models that help identify, monitor, and mitigate risks across the organization Partner with cross-functional stakeholders to investigate model performance, understand deviations from expectations, and improve decision-making processes Contribute to the development of quantitative methodologies and risk frameworks across credit, financial, and operational risk domains What makes you a great fit: M.S. or Ph.D. in a quantitative field such as Statistics, Computer Science, Mathematics, Physics, Economics, or a related discipline 4+ years of hands-on experience in Data Science, Quantitative Analytics, Risk Modeling, or a similar role Strong understanding of statistical modeling, probability theory, and uncertainty quantification Experience developing predictive models and working with financial, risk, or other highly stochastic datasets Strong proficiency in Python or R and experience with statistical and analytical libraries (e.g., Pandas, NumPy, SciPy, Statsmodels, Scikit-learn) Experience with classical statistical methods, forecasting techniques, and modern machine learning approaches when appropriate Experience modeling distributions, confidence intervals, and risk metrics rather than focusing solely on point estimates Strong problem-solving skills and ability to independently drive analytical initiatives from problem definition to implementation Excellent communication skills with the ability to explain complex concepts to both technical and non-technical stakeholders Our ways of working: Innovative Spirit: a commitment to creativity and groundbreaking solutions Honest Feedback: valuing open, transparent communication Supportive Team: a strong, collaborative community Celebrating Achievements: recognizing our wins together High-Tech Environment: a team of smart and ambitious people who challenge the status quo of traditional finance Our benefits: Relocation support to one of our hubs — Mexico, Cyprus, Spain, or Georgia — with assistance for the employee and their family Work remotely from anywhere, provided you can maintain overlap with the first half of the business day in Mexico Healthcare Coverage Education Budget: language lessons, professional training, and certifications Wellness Budget: mental health and fitness activity reimbursements Vacation policy: 20 days of annual leave and paid sick leave

Задачи: - разрабатывать и улучшать алгоритмы обнаружения фрода; - создавать и дорабатывать эвристики и ML-модели; - работать с задачами классификации текстов: экспериментировать с разными архитектурами и таргетами; - подготавливать, размечать и анализировать данные; - оценивать качество моделей и отслеживать их поведение в продакшене; - анализировать эффективность решений на данных и бизнес-метриках; - участвовать во внедрении ML-решений. Требования: - базовые знания в ML / Data Science и знакомство с задачами NLP; - умение работать с данными и писать на Python (Pandas, NumPy), знакомство с Jupyter Notebook; - владение SQL для извлечения данных; - понимание основных ML-методов и их применимости; - готовность учиться, работать в команде и принимать обратную связь; - чтение технических материалов на английском языке; - готовность к работе с большим объёмом устной коммуникации.

Location: Minsk; Warsaw; Tbilisi; Chișinău; Buсharest; Belgrade; lisbon Workplace: hybrid IDT is expanding its AI Lab and seeking a hands-on, highly experienced Senior Data Scientist with a proven track record of building new ML models from scratch and deploying them into real-time production. You will tackle complex problems across fraud prevention, payment risk, transaction scoring, personalization, and financial forecasting using large-scale transactional datasets. You will own the full modeling lifecycle — from problem framing through to production monitoring. Key Responsibilities: ML & Predictive Modeling: Design and build new models from the ground up (gradient boosting, neural networks, ensembles, anomaly detection) for anti-fraud, risk assessment, and personalization; maintain rigorous validation practices to prevent data leakage and overfitting. End-to-End & MLOps: Partner with Data Engineering and MLOps to operationalize models in production (batch and real-time/low-latency); build training, experiment tracking (MLflow), and model/data drift monitoring pipelines. Experimentation & Business Impact: Design A/B tests and statistical evaluations to measure model performance against key business metrics (reduced fraud losses, transaction acceptance rates, decision latency). R&D & Technical Leadership: Explore emerging techniques (LLMs/GenAI, graph-based methods), establish engineering best practices, and mentor fellow team members. Requirements (Must-Have): Experience: 5+ years of hands-on experience in Data Science / ML, with a strong focus on designing and building new ML models from scratch and delivering them into production with measurable business impact. English: B2+ (Upper-Intermediate or higher) — fluent written and spoken communication for technical discussions and daily collaboration with an international team. Math & Modeling Foundations: Deep understanding of statistics, probability, and validation methodologies; practical experience handling class imbalance, model calibration, and anomaly detection. Tech Stack: Python: Advanced proficiency (Scikit-learn, Pandas, NumPy); Deep Learning: Hands-on experience with PyTorch or TensorFlow; SQL: Complex queries and large-scale data manipulation; MLOps Tools: Experiment tracking and model lifecycle management (MLflow, ClearML, etc.). Education: Bachelor’s degree in Computer Science, Mathematics, Statistics, Engineering, or a related quantitative field. Nice-to-Have: Domain experience in FinTech, Payments, or Fraud Prevention: real-time transaction scoring and low-latency inference. Experience with cloud data platforms (Snowflake), Feature Stores, and event-driven architectures. Background in time-series forecasting, graph neural networks, Explainable AI (XAI), or applied LLM solutions. Master’s degree or Ph.D. in a quantitative discipline. We offer you: Remote b2b or hybrid (Belarus-Moldova) work opportunity! Stable job with long-term growth perspective. Competitive salary with annual performance review. Really good hardware. An exciting and challenging job with talented people around. Continuous learning and career growth opportunities. Compensation for professional training, seminars, and conferences. Referral program – get rewarded for helping us grow the team with talented people. Company-supported English classes to enhance your professional growth. More perks for the Minsk and Chisinau office employees. A few words about us:   IDT Corporation is a global communications company founded in 1990 and headquartered in Newark, New Jersey. We are industry leaders in prepaid communication and payment services and one of the largest international voice carriers. We are listed on the NYSE, employ over 1800 people across 20 countries, and have over $1.5 billion in revenues.    IDT is not ”another big IT corporation”— we encourage and support in-house entrepreneurs in developing their ideas into business actions.   Our flagship brand, BOSS Revolution, offers Money Transfer, International Calling, and Mobile Top-Up services and supports IDT’s mission of enabling people to keep in touch and share resources with family and friends worldwide. If you're looking for a company that will give you the maximum flexibility in choosing a location to work, this opportunity is for you!

Location: One Kattameya, Cairo Workplace: onsite We are hiring a Senior Data Scientist to join our team at Klivvr. What you'll do: - Own a domain such as credit, risk, or growth end to end, setting the modeling strategy and being accountable for the systems that result. - Take on the most ambiguous, highest-stakes problems, including framing what should be built and why before any modeling begins. - Make and defend the hard technical calls, choosing the simplest approach that solves the problem rather than the most sophisticated one. - Drive models into production and own their long-term health, performance, and reliability once they're live. - Set technical direction and standards that other data scientists build on, through design reviews, shared tooling, and engineering practices. - Mentor data scientists across levels and raise the quality bar for the team's work. - Translate business strategy into a coherent data science roadmap and communicate tradeoffs clearly to leadership. To succeed in this role, you'll need to have: - 5+ years of hands-on data science or applied ML experience, with a track record of owning systems in production end to end. - Deep proficiency in Python (NumPy, pandas, scikit-learn) and SQL, and strong software engineering fundamentals. - Strong grounding in statistics, probability, and machine learning, with the judgment to know which method actually fits the problem. - Experience designing and operating large-scale ML systems on cloud platforms (AWS preferred). - A demonstrated ability to lead ambiguous, cross-functional initiatives without close guidance. - Excellent communication skills, including the ability to influence technical and business stakeholders and to make others better through your feedback.

Обязанности: • Свод аналитики по запросу • Делать выводы по цифрам • Давать свои предложения по улучшению Требования: • Знание Excel (VLOOKUP, условное форматирование, сводные таблицы, power query) • Google Таблицы (VLOOKUP, IMPORTRANGE) • SQL (PostgreSQL/MySQL, агрегатные и оконные функции, CTE, подзапросы) • будет плюсом, Python (pandas, numpy, matplotlib, sqlalchemy, gspread). • Усидчивость • Желание разбираться и расти в аналитике Условия: • График 5/2 с 09:00 до 18:00 • Удаленный формат работы • Стабильная выплата ЗП • Возможность горизонтального и вертикального роста • Оплачиваемый отпуск • Финансовые мотивации внутри команды

Задачи: — участвовать в проектировании RAG-архитектуры под B2B-кейсы: помогать выбирать источники данных, настраивать ingestion, chunking, базовые стратегии retrieval (dense/hybrid) под руководством более опытных коллег; — настраивать LLM/агентов под предметную область: реализовывать промпты, подключать инструменты (tool calling), работать с контекстом и базовой памятью; — реализовывать POC/POV на данных клиента, проводить итерационные улучшения на основе метрик и обратной связи; — строить Data Ingestion пайплайны с OCR; — реализовывать evaluation-пайплайны: генерация тестовых наборов, запуск auto-eval, участие в human-eval; — считать и анализировать ключевые метрики (accuracy, coverage, latency, user success rate), готовить отчеты для команды; — участвовать в настройке мониторинга: логирование, базовые алерты, анализ неуспешных кейсов и их фиксы; — участвовать в интеграции с инфраструктурой (Kubernetes, облако), работать с метриками и логами; — участвовать в нагрузочном тестировании для оценки скорости инференса и необходимых ресурсов. Требования: — опыт работы с Python (pandas, PyTorch/TF, API LLM); — опыт работы в ML/DS/NLP от двух лет; — опыт работы с LLM/RAG; — понимание полного цикла ML-разработки; — практический опыт работы с RAG: настройка retrieval, работа с embedding-моделями, векторными БД (FAISS, Qdrant, Pinecone и др.); — понимание базовых техник улучшения качества (reranking, filtering, prompt tuning); — опыт работы с агентными фреймворками (LangChain / LangGraph / LlamaIndex / ADK и др.); — базовое понимание agentic-подходов и tool calling; — опыт prompt engineering и настройки LLM под задачи; — базовый опыт fine-tuning (или понимание подходов: LoRA, QLora, instruction tuning); — понимание оптимизации инференса (на уровне использования инструментов: vLLM, quantization и др.); — опыт работы с популярными ML фреймворками: tranformers, vllm, sglang, pytorch; — SQL, базовое понимание работы с данными и пайплайнами; — опыт деплоя сервисов (Docker, FastAPI), базовое понимание Kubernetes/облака; — навыки логирования, мониторинга и базовых A/B-экспериментов; — опыт работы с evaluation для ML/LLM (auto-метрики + участие в ручной оценке, LLM-as-Judge).

Задачи: - Поддержка текущего функционала продукта. - Расширение и развитие функционала действующего продукта. - Анализ логов, данных, выстраивание логических цепочек, построение и проверка гипотез. - Создание и поддержка новых дата-пайплайнов. - Работа с Clickhouse. Написание и оптимизация средних/сложных SQL запросов. - Анализ, подготовка и обработка данных. - Обеспечение качества данных. Предстоит поддерживать и развивать: - Десктопное кроссплатформенное ПО (Java 17+, Swing). - Монолитное ПО, включает в себя клиентскую и серверную части (взаимодействие посредством gRPC). - Отсутствие фрэймворка, используются собственные наработки. - Организация взаимодействия с БД (PostgreSQL, ClickHouse) посредством jdbc, без подключения дополнительных библиотек. - Организация работ механизмов в многопоточном режиме с помощью ExecutorService, CompletableFuture. - Покрытие кода тестами (Mockito, JUnit). - Аналитические запросы и большие объёмы данных (~60 TB). Требования: - Опыт работы в аналогичной роли от 3 лет. - Знание теории реляционных БД, понимание логической, физической моделей БД. - Опыт оптимизации сложных запросов. - Опыт поиска проблем в работающих под высокой нагрузкой системах. - Опыт создания и поддержки ETL-процессов. - Глубокий практический опыт разработки на Python (pandas, numpy). - Опыт работы с Apache Airflow, самостоятельное создание DAG. - Опыт работы с СУБД Clickhouse, написание средних/сложных и нетривиальных SQL-запросов. - Умение самостоятельно брать задачи, по необходимости уточнять требования и доводить до финального результата. - Навык поиска оптимальных решений: умение работать в условиях неопределенности, формулировать вопросы и предлагать решения.

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.

Задачи: — Проектировать и развивать архитектуру корпоративного Data Warehouse — Строить ELT-пайплайны с использованием Python — Поддерживать Master Data Management (MDM) и качество данных — Разрабатывать дашборды и отчёты в Power BI — Управлять рабочим пространством Power BI и доступом — Взаимодействовать с командами аналитики, продукта и технологий Требования: — Опыт 3+ года в BI, Data Analyst или BI Developer — Уверенное владение SQL (PostgreSQL, BigQuery) — Опыт проектирования DWH и моделирования данных — Знание Python (pandas, SQLAlchemy, Airflow или аналогичные инструменты) — Опыт работы с Power BI (отчёты, дашборды, администрирование) — Глубокое понимание BI-систем и рабочих процессов Плюсы: — Опыт в e-commerce, игровой индустрии или цифровых продуктах — Знание Power Query, Power Pivot — Опыт с Looker Studio, QlikView — Опыт автоматизации отчётности Условия: — Работа в быстрорастущей fintech-компании — Высокая степень ответственности и реальное влияние на продукт — Международная команда и современный офис — Зарплата 5000-8000 евро net — Поддержка релокации (перелёт, проживание, виза) — Гибридный формат работы в Лимассоле