Senior Data Scientist

Job Summary Own end-to-end development of credit scorecards and decision analytics across bureau, platform, behavioural and portfolio data - from population and target definition through deployment and monitoring.

Work with Credit, Risk, Underwriting and Technology to convert model outputs into grades, approval treatment, limit, pricing, tenure and reason codes. Applied AI is a selective secondary capability for document, evidence and analytical assistance - not autonomous financial decisioning.

Key Responsibilities

• Define development populations, observation/performance windows and targets using portfolio maturity, vintage, roll-rate and business context; benchmark existing scores before recommending a new model or recalibration.
• Clean and profile data, engineer interpretable features, prevent leakage and build explainable benchmark and challenger models across bureau, platform, repayment and other approved data.
• Complete champion-challenger selection and validation using KS, Gini/AUC, calibration, stability/PSI, out-of-time and segment performance; create score scaling, grades, reasons, limitations and model documentation.
• Translate selected models into policy/BRE treatment, approval/referral/rejection, limit, pricing and tenure logic; prepare deployment artefacts, golden cases, API/UAT evidence and production-monitoring requirements.
• Develop EWS, collections, fraud/trust, propensity and portfolio analytics, and selectively support grounded NLP/LLM use cases such as document extraction, evidence retrieval and internal risk summaries.

Core Competencies

• Strong statistical discipline combined with practical credit judgement - able to distinguish predictive lift from leakage, instability or weak business meaning.
• Hands-on ownership mindset: comfortable coding, challenging data, presenting decisions and following models through production monitoring.
• Clear communicator who can explain model behaviour, limitations and business impact to Credit, Underwriting, Technology, management and assurance teams.
• Understanding of detailed data statistics methods like regression, time series, sampling theory, hypothesis testing etc.

Must-Have Requirements

• 5-8 years of hands-on Data Science / statistical modelling experience, including at least 3 years in lending, credit risk, underwriting or closely related BFSI analytics; strong Python and SQL are mandatory.
• Personally built at least one credit scorecard or underwriting model end-to-end, including population/target design, feature engineering, validation, calibration, deployment support and monitoring.
• Strong understanding of bureau and lending data, model governance, explainability, reason codes and production implementation through policy/BRE, APIs or decision engines.