• 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.