Develop and deploy advanced ML and statistical models for business problems across customer lifecycle and risk.
Build customer behaviour models including propensity (cross-sell, churn), segmentation, CLV, and credit/risk models such as prepayments.
Apply techniques such as regression, tree-based models (RF, XGBoost, LightGBM), time series, survival analysis, and deep learning.
Leverage domain expertise in UK mortgages (origination to redemption), customer lifecycle, and hedging/risk strategies.
Translate business problems into analytical solutions and actionable insights through visualization tools and dashboards. (Power BI, Tableau experience is a plus)
Work with large-scale datasets using Python, R, PySpark, SQL, and big data platforms (Spark/Hive).
Design efficient data pipelines, feature engineering processes, and ensure strong data quality/governance.
Establish frameworks for model development, validation, deployment, monitoring, and retraining.
Ensure compliance with model governance and regulatory standards (e.g., IFRS 9, Basel where applicable).
Collaborate with stakeholders across risk, product, ALM, and engineering teams.
Communicate complex analytical insights to non-technical audiences effectively.
