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.