Can anyone tell me what the interview process for Data Scientist/ML/ GEN AI Eng. in top product based companies are like?
I have 3 years of experience in data science and 2 years in Generative AI. I'm targeting top product-based companies like Mastercard, Visa, fractal and similar tech giants (not FAANG though)
What is the typical interview process for Data Scientist/ML/ GEN AI roles at these companies, and what are the key differences in their focus (e.g., technical depth, behavioral, Gen AI-specific questions)?
Or Are there company-specific preparation strategies I should consider?
Talking product sense with Ridhi
9 min AI interview5 questions
Recruiter Screening – Background check, resume discussion, role alignment.
Technical Round(s) – Covers:
Data Science concepts (EDA, Statistics, ML algorithms)
SQL + Python coding
Case studies or product-focused ML use cases
GenAI/ML Deep Dive – For GenAI roles:
LLMs, Transformers, RAG, Prompt Engineering
Evaluation techniques, fine-tuning, vector databases
ML System Design Round – Scalable ML/AI pipelines, real-time inference, infra discussions.
Behavioral/Managerial Round – Product thinking, communication, stakeholder handling, impact delivery.
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