Senior Data Scientist @ Bayer

Master’s or PhD with 5+ years in Data Science or Applied ML, delivering production-impact solutions. Strong Python and SQL skills; expertise with standard data science libraries: pandas, NumPy, scikit-learn; experience with PyTorch or TensorFlow is a plus. Hands-on with Generative AI: embeddings, prompt engineering, tool/function calling, and agent frameworks (LangChain, LangGraph, PydanticAI); experience with vector databases (pgvector and others). Solid grounding in classical ML: model selection, validation, and metrics (e.g., AUC/F1/RMSE); time series or forecasting experience is a plus. Evaluation focus: design and run offline/online tests, rubric-based GenAI evaluation, safety checks, and error analysis; familiarity with LangSmith/Langfuse or similar is beneficial. Clear stakeholder communication: requirements gathering, expectation setting, storytelling, and influencing decisions with data. Proven problem-solving skills: structuring ambiguous problems, hypothesis-driven analysis, and iterative experimentation. Basic cloud proficiency (AWS and/or Azure): storage and compute (e.g., S3/Blob, Lambda/Functions or containers), secrets, and Databricks or Spark; awareness of CI/CD (e.g., GitHub Actions). Good engineering hygiene: modular code, testing, documentation, and reproducibility. Data governance and privacy awareness. Fluent in English (written and spoken); additional languages from our team regions are a plus. Are you excited to turn complex business questions into measurable impact? We are hiring a Senior Data Scientist for the Machine Learning & Artificial Intelligence unit within Bayer’s Enterprise Data & Analytics Platform. You will combine generative AI with classical machine learning, lead rigorous experimentation and evaluation, and communicate clear, actionable insights to stakeholders- delivering high-impact AI solutions across Finance, Supply Chain, HR, Procurement, Legal, and Communications. Our international team spans Poland, Germany, Spain, and India. We work with LLMs and embeddings, classical ML, and optimization on a modern, cloud-native stack (Python, AWS, Azure, Databricks). If solving business problems with analytical rigor and applying cutting-edge machine learning and GenAI techniques excites you, this is the place. ,[Translate business needs into data science problems; define hypotheses, success metrics, and evaluation plans; communicate progress and outcomes to stakeholders., Design, build, and evaluate (Gen)AI solutions for enterprise data, including:, LLMs and AgenticAI; including prompt/chain design, tool use, and lightweight agent workflows (LangChain, LangGraph, PydanticAI, Classical ML models (classification, regression, forecasting, clustering), feature pipelines, and explainability (e.g., SHAP, RAG pipelines and vector search for knowledge use cases, Establish rigorous evaluation: offline metrics, human-in-the-loop reviews, rubric-based GenAI assessments, groundedness/hallucination checks, and online tests (A/B or interleaving)., Implement production-ready Python code with high-quality engineering standards: Git-based workflows, code reviews, automated tests, documentation, and reproducibility., Ensure monitoring and traceability: data/feature drift checks, model and prompt versioning, cost/latency tracking, and structured logging., Partner cross-functionally with Product, Data Engineers, AI Engineers, and Business stakeholders to drive adoption for high outcome, production-grade AI solutions, Champion problem-solving and analytical rigor; lead workshops, stakeholder demos, and clear storytelling around insights, risks, and trade-offs.] Requirements: Python Additionally: Private healthcare, Sport subscription, Canteen, Modern office.