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

KSA INC

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SENIOR DATA SCIENTIST || Fintech || Mumbai