Role purpose As a Scorecard Developer, you’ll develop and maintain credit scoring components and associated calibrations that support approval and risk strategies across products and markets. You’ll focus on building high-quality features, ensuring scores are stable and explainable, and delivering robust PD-to-bad-rate calibrations that translate model outputs into decision-ready risk measures. Key responsibilities · Develop and maintain scoring solutions and supporting artefacts used in credit decisioning (application and/or behavioural scoring, segmentation, risk signals). · Own feature engineering for scoring: create, test and document variables from bureau, application, transactional and repayment data; ensure stability, interpretability and data quality. · Contribute to model development and tuning using modern machine learning approaches where appropriate, ensuring outputs are robust, stable and suitable for decisioning. · Apply best-in-class machine learning practices for credit scoring, including disciplined hyperparameter optimisation, robust validation, and repeatable model selection workflows appropriate for production decisioning. · Define and maintain feature specifications for production (definitions, transformations, edge-case handling, missing value logic, consistency checks). · Produce PD / score calibrations to observed bad rates (overall and by segment), including calibration curves, stability tracking, and recalibration recommendations. · Support cut-off / limit strategy analysis using calibrated risk outputs (approval rate vs bad rate vs loss trade-offs). · Run ongoing monitoring: drift and stability of inputs/features, score distribution shifts, performance by segment and cohort/vintage, data pipeline health. · Partner with Engineering / Decisioning teams to operationalise scoring outputs and ensure reproducibility (versioning, back-testing, change control). · Maintain clear documentation suitable for internal review/audit (feature catalogue, calibration approach, monitoring packs, change logs). Required experience and qualifications · 2–4 years’ experience in credit scoring / risk modelling / decisioning analytics in a lender, bank, bureau, or fintech setting. · Strong SQL plus Python/R for feature engineering, analysis, monitoring and calibration work. · Practical experience with advanced machine learning concepts (e.g., ensemble methods, feature selection, hyperparameter tuning, cross-validation) and the discipline to balance predictive power with stability and governance needs. · Experience translating model outputs into business-ready risk measures via calibration and performance tracking. · Ability to produce implementation-ready specifications and work closely with engineering/decisioning stakeholders. Nice to have · Exposure to multi-country portfolios and different bureau ecosystems. · Familiarity with model risk governance, validation support, and evidence pack preparation. · Experience with real-time/batch scoring pipelines and feature stores. Personal attributes · Detail-oriented and quality-driven; enjoys building reliable, production-ready data logic. · Practical communicator who can translate analytics into deployable specs and monitoring. · Comfortable operating across analytics + implementation + monitoring. Reporting line and location · Reports to: Credit Risk Modelling Lead / Scorecards Lead. · Location: Mumbai, India; collaboration with product and in-country credit risk teams.

Scorecard Developer (Machine Learning Specialist) - Bengaluru

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