Senior Data Scientist

FinBox; Bengaluru; On-site; INR 1,800,000 - 2,700,000; Full time

Job summary

FinBox, a fintech unicorn-in-progress, is seeking a data scientist/engineer to build predictive credit models and underwriting signals in a lending-focused platform.

You will integrate GST/ITR data with bank statements and bureau signals, develop risk scenarios, and ship model-based components into production for MSME and other borrower types.

Qualifications

• 4+ years total experience in fintech/data science, with 3+ years in lending/runding domains.
• Experience shipping models (risk, fraud, propensity) into production.
• Fluency with Indian credit data sources (GST, ITR, bank statements, bureaus).

Responsibilities

• Turn alternative data into financial identity and Customer 360 profiles.
• Build high-accuracy income and fraud frameworks for salaried, self-employed, and MSME borrowers.
• Fuse GST, ITR, bank statements and bureau data into underwriting signals.
• Develop propensity and credit-risk models; create a Loan Recommendation Engine.
• Optimize channel economics to reduce costs and improve outreach performance.

Skills

Model development in credit risk

Production-grade ML models

Data integration (GST, ITR, bank data)

SQL & Python

Tools

Python

SQL

Spark

Job description

FinBox, founded in 2017, is a Series B–funded fintech building infrastructure for lending products. Our platform enables banks, NBFCs, and enterprises to launch credit services quickly while expanding across India, Vietnam, Indonesia and the Philippines.

Responsibilities

• Turn alternative data into financial identity – convert consented SMS and app‑metadata signals into accurate financial profiles and a Customer 360 view for customers invisible to traditional bureaus.
• Build high‑accuracy income and fraud frameworks across borrower types, including salaried, self‑employed and MSME borrowers.
• Fuse GST, ITR, bank statements and bureau data into a single underwriting‑grade signal set, resolving conflicts across sources.
• Develop propensity, credit‑risk models and a Loan Recommendation Engine (LRE) that matches the right product to the right customer, improving take‑up and repayment rates.
• Optimize channel economics (e.g., WhatsApp outreach) to reach customers with the right nudge at the lowest cost.

Mandate Self Obligations

• Own MSME, LAP & micro‑LAP underwriting models – build income‑estimation and affordability models, encode underwriting policy in code, and guide credit teams on risk assessment.
• Develop the banking‑statement‑driven cash‑flow engine – read statement‑level metrics, self‑propagate income surrogates, and capture receivables stress early.
• Build bureau‑integrated propensities – blend commercial bureau (CIBIL MSME Rank / CMR) with consumer bureau data, mine credit‑propensity signals, and design scoring for thin‑file / new‑to‑credit MSMEs.

Who you are

• 4+ years total experience, with at least 3 years in India’s financial‑services/fintech/lending domain.
• Builder mindset – you have shipped models (risk, fraud, propensity, or similar) into production.
• Fluent in Indian credit data – GST, ITR, bank statements, bureau – and how lending decisions are actually made.
• Quantitative underwriting depth, especially MSME – able to translate underwriting into code and mentor others.