Senior Data Scientist - Databricks

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.