Scorecard Developer (Machine Learning Specialist) at SMT INC

Mumbai, maharashtra, India -

Full Time

Start Date

Immediate

Expiry Date

01 Sep, 26

Salary

0.0

Posted On

03 Jun, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Credit Scoring, Machine Learning, SQL, Python, R, Feature Engineering, Model Calibration, Risk Modelling, Hyperparameter Optimisation, Cross-Validation, Ensemble Methods, Data Quality, Model Monitoring, Decisioning Analytics, PD-to-bad-rate Calibration, Model Governance

Industry

Software Development

Description

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.

Responsibilities

Develop and maintain credit scoring components, features, and calibrations to support risk strategies across various products and markets. Monitor model stability and drift while partnering with engineering teams to operationalize scoring outputs.