Data Scientist вакансии - удалённо

Gradient Labs — AI-платформа для финансовых сервисов, которая автоматизирует customer support и back-office процессы. В компании работают русскоязычные ребята. Размер команды — 11-50 человек, инвестиции — $26M.

Компания Mira, специализирующаяся на улучшении гормонального здоровья с помощью технологий искусственного интеллекта, ищет основного продуктового дата-сайентиста для удалённой работы в рамках договора с подрядчиком в пределах ЕС. В обязанности входит разработка и внедрение аналитических моделей, направленных на отслеживание и понимание гормональных изменений на разных этапах жизни пользователей. Кандидат должен обладать глубокими знаниями в области анализа данных и машинного обучения, а также опытом работы с продуктами, связанными с биомедицинскими данными. Работа предполагает тесное взаимодействие с командой разработки и продукт-менеджерами для создания инновационных решений в сфере здоровья.

Wolt развивает платформу быстрой доставки еды, продуктов и других товаров из ресторанов и магазинов через мобильное приложение и сайт. С 2022 года Wolt входит в группу DoorDash и работает в десятках стран. Требования: — 5+ лет опыта — Владение Python или R — Знание SQL — Опыт работы с Airflow Формат работы: удалённо или в офисе в Берлине.

Ищем сильного специалиста по табличным данным в команду, которая моделирует спрос и поведение розничного портфеля крупного Банка: оцениваем, как меняется спрос на продукт при изменении его параметров, и переводим это в решения по портфелю.

Требования: — 4+ года опыта работы дата-сайентистом — Практический опыт с предсказанием LTV или ROAS — Опыт в adtech или мобильном performance-маркетинге — Уверенные навыки Python и SQL — Опыт работы с облачными хранилищами данных в продакшене — Опыт использования моделей, применяемых для реальных решений — Владение английским языком на уровне B2+ (письменно и устно)

Responsibilities: • Build and improve biometrics, algorithms, and data-driven insights for the Mira App. • Analyze hormone, cycle, app behavior, and user outcome data to identify patterns and product opportunities. Требования к кандидату: - 3+ years of experience in data science - Strong Python and SQL skills - Experience with health or wearable data - Experience in consumer health or medical devices - Strong model validation and performance evaluation skills

Banco Plata is a digital bank and fintech startup specializing in consumer lending, credit cards with a cashback program, and interest-free installment plans. Remote work or relocation options available to Cyprus, Serbia, Spain, Georgia, or Mexico.

Plata is a Mexican fintech startup specializing in consumer lending through the Plata Card. Remote work. Flexible working hours. Relocation support (Serbia, Georgia, Mexico or Colombia).

Location: United Kingdom; Spain; Germany Workplace: remote Help Us Build The Future of Travel At Airalo, we're making it easier for people to stay connected wherever they travel. As the world's first eSIM store, we help millions of travelers access affordable mobile data in 200+ countries and regions around the world. Today, we're a team of 400+ people across 60+ countries, building a product used by travelers every day. We've grown quickly, but we've worked hard to keep what matters: trust, ownership, and the freedom for people to do great work without unnecessary layers or bureaucracy. We're fully remote by design, genuinely global, and united by a shared mission to make travel simpler for everyone. Your Next Destination Location: Remote. Contract: Full-time, permanent. Benefits: Learn more about our benefits here in this link - https://airalo-public.notion.site/Benefits-25396a97ffca81fb9bc1f0be479f1be3?pvs=74  Languages: English is our main working language day to day, so you'll need to be comfortable communicating in it both in meetings and async.   We're looking for a Staff Data Scientist, Pricing to build the analytical and machine learning capability that pricing runs on, from demand modelling through to the systems that recommend price. You won't be starting from zero. We've invested in the foundational data and infrastructure that pricing decisions depend on. What's missing is the capability on top: models the business trusts, experiments that settle pricing questions with evidence, and economics defined once so they hold consistently across Commercial, Product and the customer experience. The arc of the role runs from measurement to prediction to prescription - understanding how demand responds to price, forecasting how it will respond, and ultimately building the models that recommend the price itself. What You'll Do: Build and own the demand and elasticity modelling capability, quantifying how price affects volume across destination, duration, data tier, and customer segment. Develop machine learning models for demand forecasting and willingness-to-pay, and take them from exploration through to production with the monitoring and retraining that keeps them honest. Move us from predictive to prescriptive by building the optimisation layer that turns forecasts and elasticities into recommended prices under margin, competitive and partner constraints. Design and analyse pricing experiments with statistical rigour, and apply causal methods where clean randomisation isn't possible. Define pricing within our data ecosystem, owning the governed definitions of price, cost and package economics that reporting, analysis and product surfaces all read from. Partner with Commercial and Finance to connect pricing decisions to margin and revenue, and to size opportunities before we commit. Set the analytical standard for pricing at Airalo, from what counts as evidence through to how a model or recommendation gets validated before it influences live pricing. What You'll Bring: 7+ years in data science, quantitative economics, or applied research, including pricing, monetisation, or marketplace economics work that demonstrably changed decisions. Experience with dynamic or algorithmic pricing systems in production. An advanced degree in Econometrics, Statistics, Operations Research or similar, or equivalent applied depth in demand estimation and the identification problems that make naive price-quantity regressions wrong. Hands-on machine learning experience across the full lifecycle, from feature engineering and model selection through to deployment Familiarity with optimisation and decision-science methods that turn predictions into recommended actions, whether through constrained optimisation, bandits, or reinforcement learning approaches. Proven experimentation expertise, having designed and defended experiments with a clear view on decision frameworks and common failure modes. Experience building analytical capability where none existed before, turning raw data and a business question into something a commercial team uses repeatedly. Strong data modelling instincts, thinking in reusable definitions and single sources of truth rather than standalone analyses. The ability to move comfortably between financial, product and operational data and connect the analysis to a financial outcome. Genuine partnership instincts, translating between analytical rigour and commercial reality so that Commercial and Product come to you early rather than after the decision. Fluency in the Python ML stack alongside strong SQL, with the engineering hygiene to hand over code that others can run and maintain. Excellent communication skills, including the ability to make a methodological argument to people who won't check your standard errors. A self-starter mindset that thrives in ambiguity and brings structure without waiting for permission. Comfort using AI tools to augment analytical work, with a point of view on where they help and where they don't Nice to have: Experience in marketplace, telecom, travel, or subscription/usage-based businesses. Bayesian or hierarchical modelling for sparse segments and long-tail SKUs. Competitive price response modelling, or working with scraped competitor pricing data. Familiarity with dbt, LightDash, or similar semantic/BI layers. Experience designing semantic or metric layers consumed by both analytics and production systems. Experience working in cross-functional teams spanning commercial, finance and product disciplines. If you are interested in this position, please apply via the link. Why People Join Us We started Airalo to make staying connected effortless, wherever you are in the world. Today, millions of travelers rely on our eSIMs, and many of the people building the product were customers first. Our team spans 60+ countries, united by a shared belief that great work happens when people are trusted to own what they do. There's trust to own what you do, space to focus without constant layers of process, and a clear sense of what you're responsible for from day one. As Airalo continues to grow, so do the opportunities to learn, take on new challenges, and make a real impact - from anywhere in the world. Before You Hit Apply By applying, you acknowledge and agree that, in case of successful application, Airalo may request to run background checks as a condition for entering into an agreement with you. Rest assured that these checks will only occur upon your prior consent and at the end of the selection process, and will be strictly limited to what is allowed under the laws that are applicable to you. All data that you share or that we collect in connection with such checks will be processed in accordance with our Privacy Policy, available here: www.airalo.com/more-info/privacy-policy?srsltid=AfmBOooBT0rXAj1FaNelZ3VfN0wvhwzvAoxdtHnOKSVETpiSjiXVuycy    We sincerely thank all applicants in advance for submitting their interest in this opportunity. Airalo is an equal-opportunity employer and values diversity, equity & inclusion. We do not discriminate on the basis of race, religion, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. We are committed to providing reasonable accommodations upon request for individuals with disabilities throughout our job interview process. At Airalo, we believe in roaming-free recruitment. Just as we provide clear connectivity for travelers, we provide clear salary ranges for our candidates. 🌍 This role is remote and open to candidates across our supported locations in Europe. The range below reflects the full salary range for this role across the countries where it’s available: 📍UK: £93,200 - £139,800 📍Spain: €77,600- €116,400 📍Western/Northern Europe (Germany, France, Austria, Denmark, Sweden, Finland, Norway): €66,400 - €166,800 📍Southern Europe (Portugal, Italy, Greece, Malta): €70,000 - €137,000) 📍Central & Eastern Europe (Czechia, Hungary, Poland, Lithuania, Estonia): €60,000 - €117,000 📍South-Eastern Europe / Balkans (Romania, Serbia, Bulgaria): €56,000 – €100,000 Your Home Destination: 🛫  Local market rates and cost of living vary. Your specific range within this band depends on where you’re based, and there’s room to discuss what’s right for the person and the role. We’ll walk through your range together on your first call with our Talent team so we’re aligned for takeoff! 🎒✨

Задачи: — Решать сложности бизнес-заказчиков и оценивать полученный результат. — Планировать работу команды (Data Scientists, ML-engineers). — Проектировать развитие системы прогнозирования спроса. — Проектировать с руководителями смежных технических команд совместные интеграции. — Программировать и проводить code review. Требования: — Опыт работы в ML и DS проектах от 5 лет. — Опыт решения задач прогнозирования временных рядов. — Управленческий опыт работы. — Умение писать на Python. — Умение работать с данными на PySpark, SQL или Pandas. — Знание и понимание классических ML алгоритмов (линейная регрессия, деревья решений и градиентный бустинг). — Знание Deep Learning фреймворков (Pytorch, Transformers).

RELOCATION TO GERMANY adjoe builds the technologies behind mobile apps growth and monetization. With our core product Playtime Arcade, we’ve become the global leader in rewarded advertising, an ad unit built on a simple premise: users earn real in-app rewards for engaging with new apps. The result is one of the most effective value exchanges in adtech, connecting advertisers and publishers with over 770 million users annually. Architecting Intelligence to Optimize 200M+ Daily Decisions As the intelligence core of our engineering organization, our Data Science team doesn’t just deploy models, we engineer the fundamental decision engine that powers our platform. At a scale of 770 million users and 100,000+ predictions per second, we are solving a multi-objective optimization problem that balances user incentives, advertiser ROI, and long-term platform health in real time. Our architecture is built on a 1PB+ behavioral data lake, providing the high-fidelity input necessary to train deep learning models that predict individual user engagement with precision. We aren’t just optimizing clicks, we are dynamically calculating optimal reward structures to sustain a global value exchange. Engineered for performance, our stack leverages Tensorflow and PyTorch for model training, NVIDIA Triton to achieve sub-100ms inference. We own the full ML lifecycle from high-level research and feature engineering to deployment and A/B experimentation. Here, you will find the autonomy, the data depth, and the massive scale required to solve the most complex optimization challenges in the adtech ecosystem. Your Mission & Who We Are Looking For: — Proven track record in production ML with 5+ years in Data Science and deployed models that moved real business metrics. — Building models that predict LTV, conversion, and user behavior across 770 million users, impacting advertiser ROAS and publisher revenue. — Deep learning as primary tool with strong hands-on experience in PyTorch or TensorFlow and production deployment handling 1M+ daily predictions. — Full ML lifecycle ownership from data extraction using Trino, Spark, or AWS Athena, through model development, A/B validation, to production deployment. — Experience with Airflow or model hosting is a strong plus. — Fluent in Python and data at scale, comfortable with pandas/polars, numpy, scikit-learn, LightGBM, CatBoost, XGBoost, and advanced SQL skills. — Ability to translate product requirements into ML logic and explain model behavior and impact to non-technical stakeholders. — AdTech experience is a strong plus. What’s in It for You? — Relocation support to Hamburg, Germany for worldwide talent. — Opportunity to build infrastructure behind impactful adtech platform reaching hundreds of millions of users and powering billions of decisions daily. — Ownership of projects impacting 770M users and pushing adtech boundaries. — Fast-paced environment with multiple daily solution deployments and learning from results. — Collaborative culture with diverse global team from over 40 countries. — Enjoy daily victories and energetic work environment.

Location: Remote USA; Canada Workplace: remote ABOUT YOU We are looking for an accomplished Principal Machine Learning Engineer to join our global ML organization. In this role, you will drive innovation across our machine learning ecosystem, architect advanced ML solutions, and mentor junior ML engineers around the world. You will play a key part in shaping our technical direction—leading complex ML initiatives, elevating engineering standards, and guiding teams as they build scalable, production‑ready machine learning systems. If you are ambitious, energized by solving challenging technical problems, passionate about developing talent, and excited to influence the future of ML/AI in the video game industry, this could be the perfect role for you. ABOUT US   Xsolla is a global commerce company with robust tools and services to help developers solve the inherent challenges of the video game industry. From indie to AAA, companies partner with Xsolla to help them fund, distribute, market, and monetize their games. Grounded in the belief in the future of video games, Xsolla is resolute in the mission to bring opportunities together, and continually make new resources available to creators. Headquartered and incorporated in Los Angeles, California, Xsolla operates as the merchant of record and has helped over 1,500+ game developers to reach more players and grow their businesses around the world. With more paths to profits and ways to win, developers have all the things needed to enjoy the game. For more information, visit xsolla.com. Requirements: Modeling Depth Advanced Degree in Statistics, machine learning or related areas. Experience in statistics/ML expertise with a track record of leading high-impact data science initiatives at scale of billions of transactions. Hands-on experience creating, training and fine-tuning models not just integrating hosted model APIs. You should be able to walk through  the data, the objective, what broke, and the before/after evaluation numbers, and why the model did not perform as expected. Experience owning models in production: deployment, monitoring, drift detection, retraining — with real latency budgets, not just research notebooks. Production experience with classical ML for fraud/anomaly detection, recommendation, or churn/LTV (gradient boosting, deep learning, graph-based models). Technology Familiarity Supervised learning, transfer leaning on machine learning as well as neural networks Basic LLM knowledge, especially how to use it and where not to use it. MLOps foundations: feature stores, experiment tracking, model registries (MLflow/W&B-class), continuous training pipelines. Model serving and inference optimization (vLLM-class serving, quantization). Nice-to-Have: Publications, conference talks, or recognized open-source contributions to training/eval tooling . Graph-based fraud detection (fraud rings, device/account linkage).. Gaming, payments,  fraud, advertising  domain experience. Hands-on, up-to-date experience with modern AI tools (e.g., Claude, Copilot, Cursor) for code generation, review, and accelerating day-to-day engineering work. Benefits: We are passionate about fostering a supportive environment for our team, so we prioritize the physical, mental, and emotional well-being of our employees and their families through a comprehensive Benefits Program. This includes 100% company-paid medical, dental, and vision plans, unlimited Flexible Time Off, and a personalized career roadmap for each US employee. By investing in professional development through training and educational opportunities, we ensure that our team thrives both personally and professionally. Together, we’re not just building a business; we’re cultivating a community that values creativity, collaboration, and the transformative power of play.   Equal Employment Opportunity Statement: Xsolla is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. We do not discriminate based on race, color, religion, sex, national origin, age, disability, sexual orientation, gender identity, or any other characteristic protected by law. We consider qualified applicants with criminal histories in accordance with the Fair Chance Act.   Criminal History Consideration: For the Data Scientist position, we will conduct a background check that may include the following: Criminal history check Employment verification Education verification   Relevance to Job Responsibilities: The background check is relevant to this position because of the following role responsibilities: Accessing confidential company data Ensuring compliance with regulatory requirements   Rights Under the Fair Chance Act: Applicants are encouraged to inquire about their rights under the Fair Chance Act. If you have questions regarding our hiring practices, please contact careers@xsolla.com.

Задачи: — Анализировать опоздания и "плохо построенные" маршруты. — Писать промышленный код и выводить его в продакшн. — Общаться с заказчиком, формализовывать требования. — Креативить, предлагать новые идеи. Требования: — Уверенное знание Python и стандартных DS библиотек. — Знание хотя бы одного диалекта SQL (предпочтительней PostgreSQL). — Знание основ ML и задач дискретной оптимизации. — Высшее образование в одной из сфер: математика, компьютерные науки.

Location: Remote, California, United States, AMER From Fivetran’s founding until now, our mission has remained the same: to make access to data as simple and reliable as electricity. With Fivetran, customer data arrives in their warehouses, canonical and ready to query, with no engineering or maintenance required. We’re proud that more organizations continue to leverage our technology every day to become truly data-driven. About Us Fivetran and dbt Labs are bringing together two industry-leading companies with a shared mission: helping organizations unlock the full value of their data. Together, we’re delivering the data infrastructure layer that helps organizations move, transform, and trust their data — from the moment data moves, through every transformation, to the context teams and AI systems rely on. Fivetran helps organizations automate data movement across the systems, clouds, engines, and tools they rely on. dbt Labs pioneered analytics engineering, helping teams transform data into reliable, governed insights. Together, we support thousands of organizations as they build a trusted foundation for analytics, AI, and better business decisions. As we bring our teams and technology together, we’re building on the strengths of both companies while continuing to deliver the products and experiences our customers know and trust. It’s an exciting time to join us: we’re creating a company with the scale, talent, and technology to help more organizations put their data to work with greater speed, confidence, and impact. During this transition period, you may see references to both Fivetran and dbt Labs throughout our recruiting process as we integrate our teams, systems, and career sites. About the Role This is a unique role that sits at the intersection of economics, analytics, product, and strategy. You’ll combine rigorous quantitative analysis with customer research and commercial thinking to shape how Fivetran prices, packages, and monetizes its products. What You’ll Do Analyze product usage, deal, financial, and billing data to model pricing, packaging, and monetization scenarios. Design frameworks to assess willingness to pay, customer value, and price realization across customer segments and product lines. Conduct research on pricing strategies, packaging models, and monetization approaches across comparable and adjacent markets. Lead pricing and packaging strategy for new product launches, partnering with Product, GTM, and Finance to define commercial models Build value-based pricing and econometric models to quantify elasticity, customer ROI, and lifetime value. Design and execute pricing experiments Synthesize quantitative analysis, customer research, and competitive intelligence into executive recommendations that influence pricing strategy. Drive customer and prospect research to understand purchasing behavior, value perception, and pricing friction. Partner closely with Product, Analytics, Finance, Sales, Marketing, and Legal to deliver end-to-end pricing initiatives. Define KPIs and dashboards that measure pricing performance and identify optimization opportunities. Continuously evolve our pricing and packaging strategy to improve ARR growth, customer adoption, and value realization. Skills We’re Looking For 5–8 years designing or operating SaaS or sophisticated B2C pricing models, preferably with usage‑based or behavioral pricing experience Deep quantitative chops—an advanced degree (PhD/MSc) in Economics, Statistics, Operations Research, Applied Math, or similar Fluency in SQL and Python for large‑scale data analysis; proficiency with BI tools such as Looker or Tableau; bonus for dbt Hands‑on experience with conjoint, MaxDiff, Gabor-Granger, or Van Westendorp surveys and causal experiment design Strong attention to detail and data analysis skills – including interpreting results and recommending actions Proven record of turning complex models and analyses into recommendations and shipping changes that moved ARR, gross margin, or NRR Collaborative mindset with Product, Analytics, Finance, Sales, and Legal; you can navigate from whiteboard to contract terms with ease Strong communication and presentation skills with ability to influence at the executive level #LI-DJ1 #LI-Remote The compensation range displayed on this job posting reflects the minimum and maximum target for new hire compensation for the target position and level, and may include sales incentives or target bonuses depending on the role.  Our compensation ranges are determined by role, level, and location. Our job titles may span more than one career level. Within the range, individual compensation is determined by additional factors, including job-related skills, experience, relevant education or training, business need, market demands. The compensation range is subject to change and may be modified in the future. Your recruiter can share more about the specific compensation range for your location during the hiring process. Pay Range $209,378 — $261,723 USD Perks and Benefits 100% employer-paid medical insurance * Generous paid time away from work, including paid vacation or flexible/unlimited vacation depending on role and country, plus paid sick time, inclusive parental leave, paid holidays, including a year-end Global Week of Rest, and volunteer days off. RSU stock grants* Professional development and training opportunities Company virtual happy hours, free food, and fun team-building activities Monthly cell phone stipend Access to an innovative mental health support platform that offers personalized care and resources in areas such as: therapy, coaching, and self-guided mindfulness exercises for all covered employees and their covered dependents. * May vary by country and worker type - please reach out to your recruiter for more information Click here to learn more about Fivetran's Benefits by Region. We’re honored to be valued at over $5.6 billion , but more importantly, we’re proud of our core values of Get Stuck In, Do the Right Thing, and One Team, One Dream . Read about us in Forbes . Fivetran brings together high-quality talent across the globe to make data access as easy and reliable as electricity for our customers. We value and recognize that our customers benefit from having innovative teams made of people from many backgrounds, experiences, and identities. Fivetran promotes diversity, equity, inclusion & belonging through attracting, recruiting, developing, and retaining a diverse workforce, not only because it is the right thing to do, but because it helps us build a world-class company to better serve our customers, our people and our communities. To learn more about Fivetran’s culture and what it’s like to be part of the team, click here and enjoy our video. To learn more about our candidate privacy policy, you can read our statement here . We are committed to ensuring that all candidates have an equal opportunity to participate in our interview process. If you require accommodations at any stage of the process due to a disability, medical condition, or any other circumstance, please don't hesitate to submit your request by filling out this form . We will work with you to provide reasonable accommodations to facilitate your participation and ensure a fair and accessible interview experience. Your request and any information provided will be kept confidential and will not impact your candidacy. We look forward to hearing from you and accommodating your needs to the best of our ability.

Требования к кандидату: - Several years of experience as a Data Scientist - Strong Python skills - Strong SQL skills - Experience building production data products - Solid understanding of experimentation and statistics - Experience with modern data warehouses

Задачи: - написание сервисов для команды рекомендательных систем; - разработка систем мониторинга качества рекомендательных систем; - настройка мониторинга и алертинга качества данных; - написание unit тестов (pytest), тестов для различных участков ML пайплайна, участие в код ревью. Требования: - опыт работы с рекомендательными системами от 2х лет; - опыт полного цикла решения ML-задач: от аналитики до вывода моделей в прод (от 3-х лет); - отличные знания Python, SQL, классического ML-стека и соответствующих ему библиотек; - владение одним из DL фреймворков (torch/tensorflow/keras); - глубокое понимание рекомендательных систем: подходы, типичные проблемы, метрики, А\Б тестирование; - опыт написания Unit-тестов, разработки архитектуры и Code Review; - опыт работы с инструментами разработки и CI/CD — Git, Jenkins, GitLab, Jira.