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, explainability, 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.