Data Scientist

FinBox: Where Fintech Meets Fun!

Welcome to FinBox, the buzzing hive of tech innovation and creativity! Since our inception in 2017, FinBox has built some of the most advanced technologies in the financial services space that help lenders like Banks, NBFCs and large enterprises build and launch credit products within a matter of days, not months or years. FinBox is a Series B funded company which is expanding globally with offices in India, Vietnam, Indonesia and Philippines.

Our vision is to build the best-in-class infrastructure for lending products and help Banks & Financial Services companies across the world scale and launch credit programs that set a new standard in the era of digital finance. So far, we’ve helped our customers disburse Billions of Dollars in credit across unsecured and secured credit including personal loans, working capital loans, business loans, mortgage and education loans. FinBox solutions are already being used by over 100+ companies to deliver credit to over 5 million customers every month.

Why Should You be a FinBoxer:

• Innovative Environment: At FinBox, we foster a culture of creativity and experimentation, encouraging our team to push the boundaries of what's possible in fintech.
• Impactful Work: Your contributions will directly impact the lives of millions, helping to provide fair and accessible credit to individuals and businesses alike.
• Growth Opportunities: We are a Series B funded startup and have ample opportunities for growth, professional development and career advancement.
• Collaborative Culture: Join a diverse and inclusive team of experts who are passionate about making a difference and supporting one another.

About the role:

What you'll build: the intelligence layer behind India's next wave of lending

The opportunity. FinBox turns alternative data into credit intelligence that banks, NBFCs and digital lenders use to make lending decisions for millions of Indians, many of them invisible to traditional bureaus. The data platform is built and in production: thousands of features engineered from consented mobile and financial signals, processed at scale. The next chapter is adding more to the modeling layer on top of it and that is what this role owns. It's a rare combination: a mature, proprietary data asset and a near-greenfield modeling canvas, where the models you build translate directly into loans approved, fraud stopped, and defaults avoided.

The problems you'll own

• Turn alternative data into financial identity. Convert consented SMS and app-metadata signals into rich, accurate financial profiles and a Customer 360 for people traditional credit systems can't see.
• High-accuracy income & fraud frameworks across borrower types. Build robust income-estimation and fraud-detection frameworks that hold up for salaried, self-employed, and MSME borrowers, each with a very different data footprint.
• Multi-modal underwriting intelligence with triangulation. Fuse GST, ITR, bank statements and bureau data into a single reliable view resolving conflicts across sources to produce underwriting-grade signals.
• Propensity, credit scores & the Loan Recommendation Engine (LRE). Build propensity and credit-risk models and an LRE that uses superior algorithms to match the right product to the right customer, measurably lifting the likelihood a customer takes, and repays, a loan.
• Cost-optimised engagement. Optimise channel economics, e.g. WhatsApp outreach, so the right customers are reached with the right nudge at the right time at the lowest cost.

Underwriting deep-dive, where the domain depth lives

This is a role for someone who is both a quantitative modeler and a genuine underwriting mind, able to express credit judgement in code and lift the craft of the team around them.

• MSME, LAP & micro-LAP underwriting. Own the models and decisioning behind secured MSME lending, where borrowers are traders, self-employed professionals and small businesses with little or no formal income proof. Build cash-flow-based income and affordability models for micro-LAP and LAP, translate fair-market vs. distress-sale valuation into a risk-adjusted LTV, and encode underwriting policy - eligibility, obligations/FOIR, exposure caps etc. directly in code. Shape how CAM (Credit Assessment Memo) generation is automated and be the quantitative underwriting authority who can guide credit teams on how MSME and property-backed risk is really assessed.
• Underwriting on banking data. Build the cash-flow engine that reads a bank statement the way a seasoned credit officer would - average monthly balance, credit/debit turnover, EMI and obligation load, bounce / NSF patterns, and income stability; and turns it into income surrogates and affordability for borrowers with no formal documentation. Model the working-capital cycle (the timing gap between supplier payments and customer receipts) to catch receivables stress early, handle multi-account and group-entity structures, and reconcile banking turnover against GST. Bank-statement-derived signals routinely out-predict anything on the application form so your job is to make them production-grade.
• Underwriting on bureau data. Build risk models that blend the commercial bureau (CIBIL MSME Rank / CMR) with the promoter's consumer bureau because a strong personal score can mask a stressed business, and vice versa. Mine the DPD grid, live obligations and utilization, enquiry velocity and credit vintage into default and affordability signals, compute FOIR from trade lines, and design how thin-file / new-to-credit MSMEs are scored, precisely where bureau depth runs out and FinBox's alternate data takes over.

Why this seat is rare:

• The data is done; the models are open. You inherit a deep, proprietary alternate-data asset and build the high-value modeling layer, the highest-leverage seat in the company.
• Impact you can measure, fast. Your models ship to lenders and move real outcomes: approval rates, fraud loss, collections; not dashboards.
• Senior, founding-style scope. You'll be one of a small core of senior Ds members shaping the craft, the standards, and the people around you.

Who you are:

• >8 years total experience, with at least 5 years in India's financial-services / fintech / lending domain.
• A builder, not just an analyst: you've 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.