CRED
Credit-card rewards fintech; ML for fraud, underwriting, NLP and personalisation
Interview in a few hours? A quick sprint for CRED
AI in interviews
No public policy found. Assume no AI assistance in live rounds unless your recruiter says otherwise.
Typical interview loop
How CRED usually runs it for applied AI and LLM engineer roles.
- Focus: DSA, SQL, ML basics
Online assessment
A HackerRank-style test with a couple of algorithmic coding problems, a simple SQL join task and short ML-basics multiple-choice questions.
- Focus: ML fundamentals, project reasoning
Technical round: ML breadth and projects
An ML practitioner covers fundamentals like fitting regimes and tree ensembles, presses you on why you made each choice in your projects, and may end with a coding problem.
- Focus: Probability, stats, coding
Technical round with data science lead
A senior data scientist tests probability and statistics, then asks you to code up your solution and optimise it.
- Focus: First-principles thinking, values
Behavioural and values round
Behavioural and situational questions on your past work, your design choices, working with product and engineering, and CRED's values.
How it weights your plan
Selecting CRED when you build a plan multiplies each topic’s priority score:
Official prep and resources
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