Credit and Risk Analyst

Benchire; Bengaluru; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

Benchire is seeking a senior credit risk analytics professional to design and own underwriting rules, scorecards, and decisioning logic for a lending portfolio in India. You will work with bureau data, build predictive models, validate them, and steer governance post-deployment.

You will evaluate alternative data and run experiments to prove lift, while communicating insights to cross-functional teams, including Product and Compliance.

Qualifications

• Hands-on with bureau data (CIBIL, CRIF, Experian and/or Equifax) and understanding of tradeline data.
• Strong grasp of bureau parameters, score bands, thin-file handling, DPD history and flags.
• Ability to derive bureau attributes and test predictive power.
• Understanding of bureau data limitations and regulatory frame (RBI, DPDP).

Responsibilities

• Build and own credit risk strategies for underwriting policy, cut-offs, risk-based limits, and pricing.
• Design decisioning logic end-to-end — affordability checks, fraud screens, policy waterfalls.
• Build risk models and scorecards — data prep to deployed model. You write the code.
• Own models post-live — retrain, recalibrate and rebuild as they decay with governance.
• Evaluate alternate data and demonstrate lift over existing stack before spend.
• Run controlled experiments and monitor portfolio health and KPIs.
• Communicate to senior stakeholders and partner cross-functionally to production.

Skills

Statistics fundamentals

Logistic regression

Gradient boosting

Decision trees

Risk measurement

Reject inference

Education

Bachelor's/Master's in quantitative field

CQF or equivalent credential

Tools

Python

SQL

R

Tableau/Power BI/Looker

Excel

Git

PySpark

Hive/Presto

Job description

What You Will Do

• Build and own credit risk strategies for the lending portfolio underwriting policy, cut-offs, risk-based limits and pricing, and line management.
• Design decisioning logic end-to-end — bureau rules, affordability checks (FOIR/DBR), fraud screens, policy waterfalls — quantifying the approval-rate vs. loss trade-off of every rule before it goes live.
• Build risk models and scorecards hands-on — application, behavioural and collections scoring, from data prep and feature engineering through to a deployed, monitored model. You write the code.
• Own the models after they go live — retrain, recalibrate and rebuild as they decay. Apply model risk management discipline: validation, back-testing, documented assumptions/limitations, stability monitoring, and governance on when a model can be trusted or must be retired.
• Evaluate and onboard alternate data (AA / bank statements, device, telecom, GST, UPI, employer signals) — proving incremental lift over the existing stack before recommending spend.
• Run controlled experiments — champion/challenger, swap-set analysis, structured test-and-learn — so every strategy change has an attributable outcome; and monitor portfolio health (vintage curves, roll/flow rates, PAR/DPD, early-warning indicators, FPD, cohort loss forecasting).
• Communicate to senior stakeholders and partner cross-functionally — translate a KS statistic into a business decision, and work with Product, Engineering, Business, Collections and Compliance to take strategies from analysis to production.

Bureau & Credit Decisioning Depth (non-negotiable)

• Hands-on with CIBIL, CRIF High Mark, Experian and/or Equifax data — raw report structure, tradeline-level parsing, DPD strings and enquiry data.
• Strong grasp of individual bureau parameters and their marginal impact on decisioning — score bands and thin-file/NTC handling, DPD history and worst delinquency, trade vintage, enquiry velocity, credit hunger, secured/lending mix, utilisation, write-off/settlement/suit-filed flags, and overleverage.
• Ability to build derived bureau attributes, test predictive power (IV, WOE, univariate lift), and defend why a variable belongs — or does not — in a policy or scorecard.
• Understanding of bureau data limitations (reporting lags, quality gaps, duplicate/merged files, matching logic) and the regulatory frame — RBI Digital Lending Guidelines, fair lending, DPDP — well enough to build strategies that survive an audit.

What We Are Looking For

Quantitative & statistical foundation

• Command of statistics — distributions, hypothesis testing, confidence intervals, correlation vs. causation, and knowing when a result is noise.
• Modelling — logistic regression, gradient boosting (XGBoost/LightGBM), decision trees; bias-variance and overfitting in low-default settings.
• Risk measurement — KS, Gini/AUC, PSI/CSI, IV & WOE binning, lift/gains, segmentation, survival and vintage/cohort analysis.
• Reject inference, sample bias and selection effects — why a model trained only on approved customers can quietly mislead you.

Technical proficiency

• Python (pandas, NumPy, scikit-learn, statsmodels) — production-grade — and advanced SQL (window functions, CTEs, complex joins on large loan-level tables).
• R for statistical modelling/validation; Tableau (or Power BI/Looker) for actionable risk dashboards; advanced Excel.
• Git, scripting/automation for recurring monitoring, and big-data tooling (PySpark, Hive/Presto) for data at scale.
• Good to have: decisioning/rule engines (Blaze, Drools, FICO, in-house BRE), API-based bureau integrations, and basic MLOps/model deployment.

Experience & domain

• Minimum 3 years in credit risk analytics and modelling within lending (NBFC, bank, fintech, digital lending).
• Hands-on credit risk modelling on Indian lending data is a hard requirement — you know this market's quirks (bureau coverage, thin-file/NTC, informal-income borrowers, how Indian repayment behaviour breaks textbook assumptions). Unsecured lending portfolio experience strongly preferred.
• Demonstrated ownership of a model or scorecard that went live and moved a number (approval rate, loss rate, or profitability) — ready to walk through data, modelling choices, validation and outcome.
• Bachelor's/Master's in a quantitative discipline (Statistics, Maths, Economics, Engineering). Working grasp of unit economics — how approval rate, loss, yield, opex and collections combine into portfolio profitability.
• Great to have: CQF (Certificate in Quantitative Finance) or an equivalent formal quant credential.