Credit Risk Analyst

The candidate will have responsibilities across the following functions:

Lending strategy:

• Design the logic that decides which lenders a user sees and in what order, balancing approval likelihood, offer quality and lender economics.
• Define eligibility and figure out invisible rejection signals for various lenders.

Scoring and ML models:

• Build user quality scores and approval-probability models from bureau, SMS and bank statement features, and put them into production decisions.

Lender MIS and performance:

• Reconcile lender reports with our own data, track approval, rejection and disbursal by lender, and turn that into actions on policy and lender mix.

Funnel and conversion:

• Measure conversion at every stage from intent to disbursal, across lenders and user segments.
• Diagnose where and why users drop off, and drive fixes with product and lender teams.

Requirements:

• 1 to 4 years as a Risk Analyst, Credit Risk Analyst or Business Analyst in consumer lending: an NBFC, bank, fintech lender or lending marketplace / DSA.
• Tier-1 college preferred.
• Strong hands-on SQL and Python for analysis and light automation.
• Hands-on with credit bureau data (CIBIL / Experian / Equifax): tradelines, enquiries, scores, and how to build features from them.
• Understanding of the unsecured lending funnel and credit mechanics: policy and BRE rules, pre-approved programs, KYC and disbursal, and what drives approval and rejection at a lender.
• Ownership mindset: You can take a broad problem, define the metric, pull the data, and land a recommendation without waiting to be told each step.

Good to have:

• Built or maintained Risk scorecards, logistic or tree-based models, or user segmentation for credit decisions.
• Experience working with SMS, transaction or bank statement data.
• Prior experience working in fast-paced, tech-driven environments with a focus on financial services.

Skills

Credit, Lending, Python, Risk, SQL, Scorecards, Underwriting