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;
solid 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.

📌 SENIOR DATA SCIENTIST || Fintech || Mumbai
🏢 KSA
📍 Mumbai