Data Scientist

Role - Data Scientist 5+ years

Mandatory: 2–3 years of experience working on Short-Term Personal Loan (STPL) products

Location - Mumbai Andheri (Full-Time)

About the company

We are a listed NBFC (BSE & NSE) with 40 years of history. Alongside a co-lending / fintech partner book, we run — our own B2C short-term personal loan (STPL) product launched in late 2025. Ticket sizes are ₹1,000–₹15,000, and tenures are 3–6 months. We are a team (35–40 people) operating like a startup inside a regulated public listed NBFC.

The Data Science team owns Risk, policy, fraud signals, scorecards, and portfolio & overall analytics. We are hiring a Senior Data Scientist who has built and shipped credit decisioning systems (scorecards, BRE, feature pipelines) in digital lending.

The Role

Own end-to-end credit & fraud data science : feature engineering from raw bureau JSON ,SMS,DEVICE, scorecard / model development, Business Rule Engine (BRE) design, monitoring, and partnering with product/engineering to put rules live. You will work directly with the existing DS team, tech, product and founders — decisions are data-backed and debated.

What you will own

• 1. Build and maintain credit scorecards and models for FTB and Repeat Borrowers (Xgboost, Random forest, Support Vector Machine Models, ensemble models, challenger models).
• 2. Engineer features from raw CRIF (or equivalent) bureau JSON — tradelines, enquiries, DPD
• histories, identity matches — and from raw SMS / FinBox alt-data (collections, rejections, salary, app footprint).
• 3. Design, validate, and ship Models: hard rejects, soft flags, amount caps — with clear lift/capture / approval trade-offs.
• 4. Own portfolio risk analytics: vintage / DPD / non-starter / POS bad-rate monitoring; propose tier pauses, cool-offs, and ladder-up changes.
• 5. Build fraud signals (device, SIM/OTP, mule, ring, post-disbursal disappearance) and help prioritise the fraud PRD backlog into production.
• 6. Partner with engineering to productionise features, rules, and models (Watchtower-style shadow
• underwriting, policy index, monitoring dashboards).
• 7. Challenge and refine existing tier/ladder policy with evidence; communicate clearly to founders and business.

Required experience

● Tenure: 5+ years overall experience in data science/analytics.

● Digital lending: Minimum 3 years hands-on in digital lending/consumer credit (NBFC, fintech lender, digital/STPL) who has built models themselves.

● Scorecards/models: Built and deployed at least one credit scorecard (first-time borrower or repeat borrower, or combined model) into a live BRE / LOS. Should improve approval–bad-rate trade-offs from production experience.

• ● Bureau: Parsed and engineered features from raw bureau files (CRIF / CIBIL / Experian JSON or
• XML) — not only vendor-precomputed attributes.
• ● Non-starter models: Fraud/non-starter / First Payment default modelling experience in
• short-tenure lending.
• ● Limit Assignment: Experience with repeat-borrower ladder / limit-management policies.
• ● Monitoring and QC: Shadow underwriting/champion–challenger frameworks.
• ● Alt-data: Worked with SMS / alt-data / device / AA signals for underwriting or fraud (FinBox, similar vendors, or in-house SMS parsing).
• ● Stack: Strong SQL + Python (pandas, sklearn/Logistic / lightgbm/Xgboost/randomforest,
• statsmodels). Able to write production-quality notebooks and scripts, not just slide decks.
• ● Communication: Comfortable debating policy with founders/credit heads using data; owns the show me the evidence conversation.

Nice to have

● Feature stores, Airflow/cron pipelines, S3 + Postgres + DynamoDB.

● Prior Experience: Prior work at a zero-to-one digital lender or STPL product.

What success looks like in 6 months

• ● A documented feature dictionary from raw bureau + SMS with IV/KS ranking.
• ● At least one new scorecard/model live with clear expected vs observed bad-rate impact.
• ● Non-starter / First Payment Defaults monitoring with actionable rule recommendations and clear
• demonstrated improvements in defaults
• ● Credible pushback on weak policy ideas — backed by analysis, not opinion.

Hiring process

• 1. Screening call and technical deep dive (role fit, digital lending depth).
• 2. In-person technical deep dive + case study + portfolio discussion + overall
• 3. Founder/culture + Technical round.
• 4. Offer.