Senior Data Modeler

We are augmenting our Data Intelligence team with a Senior Data Modeller who can build statistical and machine learning models that directly support lending decisions: propensity, risk, cross-sell, and application scoring, depending on the candidate's strongest background. This role owns the model build end-to-end: from feature creation through to a validated, deployable model, working closely with data analysts and Risk, Credit, or Marketing stakeholders on model use and adoption.

Responsibilities:

• Design and build predictive models such as propensity / cross-sell models, application scorecards, or risk/delinquency models depending on domain fit.
• Engineer features from transactional, bureau, collections, or campaign data to feed model development.
• Apply and compare modelling techniques (e. g., logistic regression, XGBoost / gradient boosting, decision trees, and similar classification techniques) to select the best-fit approach.
• Validate model performance metrics, stability checks, and back-testing and document model logic and assumptions for governance and audit.
• Partner with data analysts and Risk, Credit, or Marketing stakeholders (as aligned) to translate business objectives into model design choices.
• Support model deployment, scoring pipeline handoff, and periodic model monitoring/recalibration.
• Write and optimise SQL for feature extraction and model dataset preparation.

Requirements:

• 5-7 years of experience building statistical / machine learning models in a Bank or NBFC, with lending experience mandatory (microfinance exposure not required).
• Hands-on experience building propensity, cross-sell, risk, or application scoring models using techniques such as logistic regression, XGBoost, or other tree-based / classification methods.
• Strong Python (or R) skills for model development, plus strong hands-on SQL for feature and dataset preparation.
• Experience modelling in at least one of: risk/credit, or marketing/cross-sell.
• Ability to explain model logic, drivers, and trade-offs clearly to business and risk stakeholders.

Good to Have:

• Experience with model validation, monitoring, or governance frameworks.
• Exposure to credit bureau data, collections data, or campaign/CRM data.
• Familiarity with loan lifecycle metrics (delinquency, vintage, roll-rate) or marketing metrics (funnel, attribution, CLV).
• Experience with dimensional/semantic data modelling (star schema or equivalent) as a supporting skill.
• Bachelor's or Master's degree in a quantitative discipline Statistics, Economics, Engineering, Computer Science, or related field.