We are looking for a Senior Data Scientist with experience sourcing data from Data bricks. You will own the full model lifecycle, from problem framing and experimentation through deployment and monitoring, and turn business questions into measurable impact within an agile team.
• Frame business problems as data science problems and define success metrics 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, Unity Catalog model registry, and Databricks Feature Store
• Deploy models to production via batch and real-time serving (Databricks Model Serving), with 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
• 4-8 years of data science experience, with 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 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 ability to work with international clients
• Databricks Certified Machine Learning Professional
• Experience with LLMs, RAG, and agent frameworks on Databricks (Mosaic AI)
• Exposure to data engineering and Medallion architecture (Delta Lake)
• Azure services (Azure ML, ADLS Gen2, Data Factory, Key Vault)
• Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, Data Science, or a related field
Data Science, Machine Learning, Python, Pandas, Scikit-learn, PySpark, Apache Spark, Spark SQL, SQL, Databricks, Exploratory Data Analysis (EDA), Feature Engineering, Statistical Analysis, Statistics, Probability, Hypothesis Testing, Experiment Design, A/B Testing, Classification, Regression, Forecasting, Clustering, Natural Language Processing (NLP), Machine Learning Model Development, Model Training, Model Validation, Model Evaluation, MLflow, Experiment Tracking, Model Lifecycle Management, Unity Catalog, Unity Catalog Model Registry, Databricks Feature Store, Databricks Model Serving, Batch Model Serving, Real-Time Model Serving, Lakeflow Jobs, MLOps, CI/CD, Model Deployment, Model Monitoring, Model Performance Monitoring, Data Drift Monitoring, Model Fairness, Model Retraining, Model Optimization, Data Governance, Data Quality, Data Engineering, Medallion Architecture, Delta Lake, Git, Version Control, Reproducible Code, Software Testing, Technical Documentation, Databricks Apps, Dashboards, LLMs, Retrieval-Augmented Generation (RAG), Agent Frameworks, Mosaic AI, Azure Machine Learning (Azure ML), Azure Data Lake Storage Gen2 (ADLS Gen2), Azure Data Factory, Azure Key Vault, Stakeholder Management, Data Modelling, Business Problem Solving, Success Metrics Definition, Technical Communication, Business Communication, Mentoring, Modelling Standards, MLOps Best Practices, Databricks Certified Machine Learning Professional.