Slice Senior MLE / Data Scientist Interview Guide

Slice (now operating as a Small Finance Bank) hires senior MLE / DS for credit underwriting on young / thin-file borrowers, fraud, and product analytics. The interview tests credit risk on a new demographic plus regulatory discipline. This guide covers it.

Slice has pivoted from a credit-card-like product to a Small Finance Bank, but the modeling DNA — underwriting young, thin-file borrowers — remains central. Senior MLE / DS roles cover credit scoring, fraud, transaction enrichment, and product analytics.

The loop structure (Senior MLE / DS)

Standard loop: recruiter screen → ML fundamentals + SQL → credit risk case → ML system design → hiring manager + behavioural.

Round-by-round breakdown

Round 1 — Recruiter screen. 30 min.

Round 2 — ML fundamentals + SQL. 75 minutes. Standard.

Round 3 — Credit risk case. 90 minutes. Build a credit score for a 22-year-old first-time borrower with no credit history. Probed for: alternative-data features, calibration, segment validation, regulatory awareness (now SFB licensed).

Round 4 — ML system design. 60 minutes.

Round 5 — Hiring manager + behavioural. 60 minutes.

What Slice weights distinctively

1. Young / first-time borrower modeling. Slice's core demographic has minimal bureau history. Alternative data (app usage, device, behavioral) is critical. 2. Bank-level discipline. Now an SFB, Slice has RBI compliance built in. Senior MLEs must know it. 3. Product analytics depth. Cross-functional with PM teams on feature impact. 4. Engineering quality. Slice maintains high code quality standards.

Top 10 questions Slice senior MLE / DS candidates face

1. "Credit score for first-time borrowers — what alternative data and how do you model?" 2. "RBI SFB guidelines — what changed for your modeling vs the pre-license product?" 3. "Fraud at signup — synthetic identity, mule accounts. How do you detect at scale?" 4. "Calibration vs ranking for credit. When does each matter for Slice's business?" 5. "Walk through diagnosis of approval-rate drop in a specific city." 6. "A/B testing under SFB constraints — what's allowed and what isn't?" 7. "Feature engineering from app-usage data — examples?" 8. "Demographic compliance — what features can't you use and why?" 9. "Walk through your most impactful production project." 10. "Why Slice specifically (vs other Indian fintechs)?"

The prep path through MSL

Compensation

Slice Senior MLE / DS in 2026 ranges roughly ₹30 lakh – ₹55 lakh for 5-7 YOE. Lead roles ₹70 lakh+.