Job Description:
Role Overview
We are looking for a Senior Data Scientist,for our client in the banking sector, for a 12-month contract to build and optimize end-to-end machine learning, AI, and decisioning solutions across Personal & Private Banking (PPB) and Digital channels. Leveraging the Databricks platform, you will focus on feature engineering, predictive modelling, experimentation, and decision science to deliver scalable analytical assets.
Key Responsibilities
- Model Development & GenAI: Design, train, and optimize ML/AI models (customer propensity, next-best-action, risk, fraud, and GenAI use cases) using Databricks and MLflow.
- Feature Engineering & Governance: Build and maintain reusable enterprise feature pipelines using Databricks Feature Store and Delta tables with full quality and lineage controls.
- Decisioning & Optimization: Develop decision science models to enhance customer acquisition, engagement, cross-sell strategies, and operational outcomes.
- Monitoring & Governance: Monitor model performance, stability, and drift; produce complete model documentation and governance artifacts for risk compliance.
- Stakeholder Collaboration: Partner with PPB, Digital, Risk, and MLOps teams to translate business needs into production-ready analytical solutions.
Minimum Requirements
- Education: Degree in Data Science, Computer Science, Engineering, Mathematical Statistics, Actuarial Science, Econometrics, or a quantitative field.
- Databricks Stack: Hands-on experience using Databricks (Notebooks, Workflows, MLflow, and Feature Store/Delta tables).
- Technical Skills: Strong proficiency in Python and SQL for large-scale data manipulation and machine learning.
- Domain Modelling: Proven experience building customer (propensity, next-best-action, retention) or operational/risk models in financial services.
- End-to-End Execution: Demonstrated track record of feature engineering, model tuning, validation, governance, and productionizing models alongside engineering teams.