Lead - Data Modeling

Hiring for: One of India’s leading non-banking financial companies (NBFCs), focused on driving financial inclusion across rural and semi-urban markets.

Role: Lead - Data Modeling

Experience: 10 to 15 years

Location(s): Kurla, Mumbai

Salary: Up to INR 70 LPA

1. Job Purpose Statement

The Lead – Data Modeling will drive the full lifecycle of model development across Credit Risk, Behavioural, Collections, Propensity, Fraud, and ML based decisioning frameworks. The role ensures strong model governance, documentation, deployment excellence, and audit readiness while partnering with Credit, Risk, Fraud, Collections, and Technology teams.

2. Duties & Responsibilities

Model Development:

·      Build and lead development of credit risk scorecards, acquisition models, and line assignment models.

·      Develop behavioural risk models predicting delinquency, repayment patterns, and customer performance.

·      Create collections scorecards, propensity-to-pay and propensity-to-roll models.

·      Design PAPQ strategies for targeted customer interventions.

·      Build fraud risk models for application, transaction, and behavioural fraud detection.

·      Conduct rejects inference analysis to strengthen development datasets.

Governance, Validation & Compliance:

·      Own Model Inventory Management—versioning, approvals, governance checks.

·      Ensure model validation, back-testing, performance tracking, and stability assessments.

·      Maintain complete model documentation in line with audit and regulatory standards.

·      Manage a central repository for audits, external validations, and compliance reviews.

Deployment & ML Ops:

·      Lead UAT cycles, scenario testing, and signoffs for scorecard deployments.

·      Work with Tech to implement ML Ops frameworks for automated deployment and monitoring.

·      Ensure smooth integration of models into production systems.

Reporting & Insights:

·      Publish monthly login and scoring summaries with model performance insights.

·      Present population stability, score distribution, model drift, and early warning signals to leadership.

Leadership & Collaboration:

·      Mentor a team of analysts/modelers and drive capability building.

·      Collaborate closely with Credit Risk, Collections, Fraud, Product, and Technology teams.

·      Present strategies, model outcomes, and risk impacts to senior stakeholders

3. Decision Making Authority

Decision made Independently:

-         Selection of modeling techniques, algorithms, and variable transformations best suited for a given business problem.

-         Designing model development approaches, feature engineering logic, and rejecting/retaining variables based on statistical merit.

-         Prioritizing day‑to‑day tasks for the modeling team and assigning work to analysts/modelers.

-         Choosing the right validation, back‑testing, and performance‑tracking methodologies.

Decisions made in consultation with manager:

-         Final sign‑off on new credit/collection/fraud models before deployment into production.

-         Decisions involving major policy changes, risk thresholds, or cut‑offs that impact business sourcing or portfolio behaviour.

-         Approvals for model‑related governance documentation, audit responses, or regulatory submissions.

-         Long‑term modeling roadmap, resource planning, and prioritization of strategic analytics initiatives.

4. Job Requirements

Professional Qualification:

-         Bachelor’s/Master’s in Statistics, Data Science, Mathematics, Economics, or related fields.

-         10+ years of experience in credit risk modeling, fraud modeling, ML, or advanced analytics.

 Knowledge:

-         Strong proficiency in Python/R, SQL, and statistical/ML techniques (LR, GBM, XGBoost, etc.).

-         Deep understanding of credit lifecycle, collections behaviour, and fraud patterns.

-         Experience managing end-to-end model development, governance, and deployment.

 Skills:

-         Advanced Statistical & ML Expertise, Risk Modeling Expertise, Data Handling & Feature Engineering, Model Governance & Compliance

-         Analytical Thinking & Problem-Solving, Decision-Making & Accountability, Communication & Presentation, Stakeholder Management