Data Scientist

Data Scientist – Risk

*Experience:* 4–5 Years

*Industry:* FinTech / Digital Lending / NBFC / Banking

About the Role

We are looking for a *Data Scientist – Risk* with 4–5 years of experience in the *FinTech/Lending industry. The ideal candidate should have strong expertise in **data analytics, machine learning, Python, SQL/PostgreSQL, and a solid understanding of **credit and lending risk*.

The candidate will work closely with Risk, Credit, Business, Product, and Collections teams to build data-driven solutions and improve lending decisions.

Key Responsibilities

* Analyse customer, loan, repayment, transaction, and bureau data to identify *credit-risk trends and opportunities*.

* Develop and monitor *credit-risk and predictive ML models* for default, risk segmentation, collections, fraud, etc.

* Perform *portfolio, vintage, cohort, delinquency, roll-rate, and default analysis*.

* Build features, train, validate, and monitor ML models using appropriate metrics such as *AUC, Gini, KS, Precision/Recall, Lift, and PSI*.

* Write complex *SQL/PostgreSQL queries* for data extraction, analysis, and reporting.

* Use *Python (Pandas, NumPy, Scikit-learn, XGBoost/LightGBM, etc.)* for analytics and modelling.

* Create and maintain *risk/portfolio dashboards* using Metabase or other BI tools.

* Translate analytical findings into actionable recommendations for *credit policy, underwriting, risk strategy, and collections*.

Core Skills

* *4–5 years of experience in FinTech/Lending/NBFC/Banking/Credit Risk*.

* Strong understanding of *credit risk and lending lifecycle*.

* Strong hands-on experience with *Python, SQL/PostgreSQL, Data Analytics, and Machine Learning*.

* Experience with *ML models*.

* Strong knowledge of *feature engineering, model evaluation, and statistical analysis*.

* Understanding of key lending metrics: *DPD, PAR, FPD, NPA/Default, Roll Rates, Vintage Analysis, and Recovery/Loss rates*.

* Strong analytical, problem-solving, and stakeholder communication skills.

* Exposure to *credit bureau data, fraud analytics, or collections analytics*.

* Knowledge of *model monitoring, explainability, and model/data drift*.

Ideal Candidate

A candidate who can independently take a problem from:

*Risk/Business Problem → Data Analysis → Feature Engineering → ML Model → Validation → Business Recommendation*

and has a strong combination of *Lending/Risk domain knowledge + Data Science + SQL + Python

Pay: ₹500,000.00 - ₹1,000,000.00 per year

Work Location: In person