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YOUR ROLE

  • Frame business problems as data science problems and define success metrics together with stakeholders.
  • Perform exploratory data analysis, feature engineering and statistical analysis on large datasets using Databricks, PySpark and Spark SQL.
  • Develop, train and validate ML models (classification, regression, forecasting, clustering, NLP) with rigorous experimentation and evaluation.
  • Track experiments and manage the model lifecycle using MLflow, the Unity Catalog model registry and Databricks Feature Store.
  • Deploy models to production via batch and real-time serving (Databricks Model Serving), applying CI/CD and MLOps best practices.
  • Monitor model performance, data drift and fairness; retrain and improve models over time.
  • Collaborate with data engineers, analysts and architects to ensure reliable, well-governed data for modelling.
  • Communicate insights and model results to technical and business audiences, including dashboards and apps built on Databricks Apps.
  • Maintain code in Git, write reproducible, tested code and document models and assumptions.
  • Mentor junior data scientists and contribute to modelling standards and best practices.

YOUR PROFILE

  • 4–8 years of data science experience, including at least 1–2 years of hands-on Databricks experience.
  • Strong Python (pandas, scikit-learn) and PySpark; strong SQL.
  • Solid foundation in statistics, probability, hypothesis testing and experiment design (A/B testing).
  • Hands-on experience with MLflow, Databricks Feature Store, Unity Catalog and Model Serving; Lakeflow Jobs for orchestration.
  • Proven experience taking ML models from prototype to production (MLOps).
  • Strong communication skills and the ability to work with international clients; very good English.
  • Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, Data Science or a related field.

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