Senior Data Scientist

Why work at Nebius Nebius is leading a new era in cloud computing to serve the global AI economy. We create the tools and resources our customers need to solve real-world challenges and transform industries, without massive infrastructure costs or the need to build large in-house AI/ML teams. Our employees work at the cutting edge of AI cloud infrastructure alongside some of the most experienced and innovative leaders and engineers in the field. Where we work Headquartered in Amsterdam and listed on Nasdaq, Nebius has a global footprint with RD hubs across Europe, North America, and Israel. The team of over 1400 employees includes more than 400 highly skilled engineers with deep expertise across hardware and software engineering, as well as an in-house AI RD team. The role Nebius is looking for a Senior Data Scientist to build and deploy time series forecasting and classical machine learning models that support core business planning and operational decisions. You will own problems end-to-end, from data exploration and feature engineering to model deployment and monitoring, working with large-scale, real-world datasets. The role emphasizes strong statistical thinking, robust modeling, and production reliability over experimentation with trendy frameworks. Your work will directly impact forecasting accuracy, resource planning, and key business metrics. You’re welcome to work in our offices in Tel Aviv. Your responsibilities will include: Time series forecasting. Design, build, and maintain forecasting models (e.g., demand, usage, capacity). Evaluate performance using appropriate metrics and improve accuracy over time. Classical ML modeling. Develop and deploy models using regression,Binary classifcatiom , tree-based methods, and other well-established approaches where they provide the best trade-off between performance, interpretability, and maintainability. Feature engineering data preparation. Build robust pipelines for data cleaning, transformation, and feature generation (including time-based features, seasonality, and external signals). Model evaluation monitoring. Define evaluation frameworks, backtesting strategies, and monitoring to ensure models remain stable and reliable in production. Production MLOps. Deploy models into production environments, collaborate on pipeline orchestration, and ensure reproducibility and version control. Exploratory analysis. Analyze large datasets to identify patterns, anomalies, and drivers of change in time-dependent behavior. Stakeholder collaboration. Work closely with business and product teams to define forecasting needs, align on success metrics, and translate model outputs into actionable insights. Cross-domain contribution. Contribute to adjacent data science areas (e.g., LLM-based systems) as needed, ensuring shared ownership and continuity across team responsibilities. We expect you to have: Experience as a data scientist working in production environments (5+ years). Experience with time series modeling and forecasting techniques. Experience building reliable data pipelines and working with large datasets. Experience with modern data science tools and ecosystems using Python (e.g., NumPy, Pandas, Scikit-learn, deep learning frameworks). Strong SQL skills and experience working with large datasets. Strong foundation in statistics and machine learning fundamentals. Background in Computer Science, Statistics, Mathematics, Industrial Engineering, Economics, or a related field (Master’s degree or equivalent). Demonstrated ability to take models from idea to production and ensure ongoing value. Demonstrated ability to apply structured thinking and robust evaluation in forecasting problems. Strong communication skills and ability to explain model outputs, uncertainty, and limitations. Working knowledge of spoken and written English. It will be an added bonus if you have: Experience working in cloud environments (preferably Azure). Experience wit ... (truncated, view full listing at source)