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.