SENIOR DATA SCIENTIST || Fintech || Mumbai

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.

Skills: calibration,underwriting,risk,credit scoring,fraud,credit,data,data science,models