About the role :

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, explain ability, reason codes and production implementation through policy/BRE, APIs or decision engines.

- Exposure to logistic scorecards and ML methods such as XGBoost/CatBoost/LightGBM, along with cloud, MLflow, APIs, Git and MLOps practices.

- B.e/B.Tech/B.Stat.

Good-to-Have (Optional) :

- Experience in MSME, embedded finance, line-of-credit, working-capital, collections, fraud or early warning analytics.

- Practical exposure to NLP/LLM, RAG or document-intelligence use cases with grounding, evaluation, privacy controls and mandatory human review.

Ideal Candidate Profile :

- A credit modeller first : technically strong, commercially aware and able to translate statistics into defensible lending treatment.

- Has worked with imperfect real-world data and can make transparent decisions on exclusions, missingness, stability, segments and implementation trade-offs.

- Uses Applied AI selectively where it improves analysis or productivity, without over-engineering the role or weakening model and policy governance.