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