Verified Job
IT / Software / Data Analyst
Snapmint - Data Scientist - Credit Risk Team
IT / Software / Data Analyst
#746664
Remote / WFH
Snapmint Credit Advisory · Bengaluru, Karnataka
Job Description
Job description
Job Description - Data Scientist (Credit Risk Team)
Location: Bangalore
About Snapmint:-
India's booming consumer market has over 300 million credit-eligible consumers, yet only 35 million actively use credit cards. At Snapmint, we are building a powerful alternative to credit cards-enabling consumers to Buy Now, Pay Later with zero-cost EMIs across categories like fashion, electronics, and lifestyle products.
Founded in 2017, Snapmint is India's leading online zero-cost EMI provider, serving 10M+ consumers across 2,200+ cities, and growing 2x YoY. Our founders are IIT Bombay & ISB alumni with deep experience across Swiggy, OYO, Maruti Suzuki, and ZS Associates, and a strong track record of scaling and exiting multiple ventures.
About the Role:-
We are hiring a Data Scientist - Credit to own end-to-end credit underwriting models for a high-scale fintech product. This role involves model development, real-time deployment, monitoring, and continuous optimization, with direct impact on approval rates, fraud, credit losses, margins, and GMV.
Key Responsibilities:-
Credit Underwriting & Modeling
1. Build, deploy, and maintain credit risk models using Logistic Regression, XGBoost / LightGBM, and Deep Learning
2. Design underwriting strategies for NTC (New-to-Credit) users using alternative and behavioral data
3. Balance credit risk, fraud risk, and conversion across checkout journeys
4. Design real-time features with strict latency SLAs (<100 ms), including:
- Velocity & frequency features
- Rolling window aggregates
- Behavioral & transactional signals
5. Implement incremental / online feature updates (mean, std, EWMA, decay)
6. Ensure training-serving parity using feature stores or custom pipelines
7. Partner with Risk & Business teams to define:
- Score cut-offs, bands, and guardrails
- Policy rules layered on top of ML models
- Experimentation & Business Impact
8. Design and evaluate experiments to measure impact on:
- Approval rates
- FPD & delinquency
- Portfolio losses and margins
What We're Looking For:-
- 2 - 6+ years of experience in Credit Risk / Lending / Cards
- Strong hands-on experience with Python & SQL
- Experience working with credit bureau data (CIBIL / Experian / Equifax)
- Strong understanding of risk metrics, model evaluation, and monitoring
- Ability to translate business problems into scalable ML solutions
Working Days : Monday to Friday.-
Job Description - Data Scientist (Credit Risk Team)
Location: Bangalore
About Snapmint:-
India's booming consumer market has over 300 million credit-eligible consumers, yet only 35 million actively use credit cards. At Snapmint, we are building a powerful alternative to credit cards-enabling consumers to Buy Now, Pay Later with zero-cost EMIs across categories like fashion, electronics, and lifestyle products.
Founded in 2017, Snapmint is India's leading online zero-cost EMI provider, serving 10M+ consumers across 2,200+ cities, and growing 2x YoY. Our founders are IIT Bombay & ISB alumni with deep experience across Swiggy, OYO, Maruti Suzuki, and ZS Associates, and a strong track record of scaling and exiting multiple ventures.
About the Role:-
We are hiring a Data Scientist - Credit to own end-to-end credit underwriting models for a high-scale fintech product. This role involves model development, real-time deployment, monitoring, and continuous optimization, with direct impact on approval rates, fraud, credit losses, margins, and GMV.
Key Responsibilities:-
Credit Underwriting & Modeling
1. Build, deploy, and maintain credit risk models using Logistic Regression, XGBoost / LightGBM, and Deep Learning
2. Design underwriting strategies for NTC (New-to-Credit) users using alternative and behavioral data
3. Balance credit risk, fraud risk, and conversion across checkout journeys
4. Design real-time features with strict latency SLAs (<100 ms), including:
- Velocity & frequency features
- Rolling window aggregates
- Behavioral & transactional signals
5. Implement incremental / online feature updates (mean, std, EWMA, decay)
6. Ensure training-serving parity using feature stores or custom pipelines
7. Partner with Risk & Business teams to define:
- Score cut-offs, bands, and guardrails
- Policy rules layered on top of ML models
- Experimentation & Business Impact
8. Design and evaluate experiments to measure impact on:
- Approval rates
- FPD & delinquency
- Portfolio losses and margins
What We're Looking For:-
- 2 - 6+ years of experience in Credit Risk / Lending / Cards
- Strong hands-on experience with Python & SQL
- Experience working with credit bureau data (CIBIL / Experian / Equifax)
- Strong understanding of risk metrics, model evaluation, and monitoring
- Ability to translate business problems into scalable ML solutions
Working Days : Monday to Friday.-
