How Indeed Compares to FAANG in Data Scientist Interviews (My Experience)

Photo by appshunter.io on Unsplash

I’ve always heard good things about Indeed’s data culture — thoughtful, product-driven, and grounded in real impact. So when I got the chance to interview for the Data Scientist II role early this year, I went all in. What followed was a multi-round process that pushed me technically, made me reflect on my past work, and ultimately taught me a lot. Also, I feel Indeed is an underrated company. Its perks are similar or even better than the FAANGs.

This is my honest experience — no insider secrets or question leaks, just what the journey felt like.

Round 1: Case Study — Understanding the Product Mindset

The first round was centered on a case-study discussion around aspects of Indeed’s job marketplace. What I appreciated was how open the conversation was. It wasn’t about “Do you know this feature?” It was about How would you think about this problem if you had to design it?

The interviewer really wanted to see structure, reasoning, and clarity — not trivia. A refreshing start.

Round 2: Resume Deep Dive (In-Person)

This round was exactly what it sounds like — a deep exploration of one of my major projects. I walked through the entire lifecycle: the problem, the data, the design decisions, and the technical choices.

What stood out were the follow-up questions. They wanted to understand the “why” behind database selections, architectural patterns, and trade-offs. It reminded me that for mid-level DS roles, depth matters just as much as breadth.

If you’ve ever worked on a project where you took shortcuts “because it worked,” this round will reveal it.

Round 3A: Whiteboard Coding

Next came a hands-on coding design session. I had to implement a small class-like structure and think through how it should behave, how data flows through it, and how to keep it clean and efficient.

It wasn’t the difficulty of the problem that made it challenging — it was doing everything from scratch while explaining my thought process out loud.

I actually enjoyed this round. It felt like pair programming, just with markers instead of keyboards.

Round 3B: Math & Statistics

Yes, pure math this time! This round covered fundamentals — probability, inference, regression, and experiment evaluation — but all wrapped in real-world scenarios rather than formula memorization.

The interviewer pushed for intuition:

• Why would you choose one approach over another?
• What assumptions matter?
• How do you interpret the results?

Nothing felt unfair. If you’re grounded in stats, you’ll find this round intellectually satisfying.

Round 4: Machine Learning Whiteboard

This was the heaviest technical round for me. It began with implementing a basic evaluation metric using NumPy, and then moved into building a simplified training loop for a model — no ML libraries allowed.

This round is all about fundamentals: matrix shapes, gradient intuition, iterative updates, and clear logic.

And this is where I stumbled. I understood the concepts, but stitching everything together into a clean, working solution under time pressure didn’t go as smoothly as I hoped.

Looking back, this round taught me exactly what I needed to revise.

Round 5: Behavioral + Closing Conversation

This round was warm, conversational, and honest. We talked about working style, collaboration, past experiences, and what I value in a team. I had the chance to ask questions about culture, growth paths, and what success looks like in the role.

It felt less like an interview and more like a mutual evaluation — and I loved that.

The Outcome (And What I Learned)

About a month after the last round, the recruiter reached out with thoughtful feedback. I had done well in most rounds, but the ML whiteboard round held me back. They decided not to move forward, but they encouraged me to reapply after the six-month cooling period.

Was I disappointed? Absolutely. But I also appreciated the transparency — and I walked away with a clear sense of what to strengthen before my next attempt.

Final Thoughts

If you’re preparing for a Data Scientist interview at Indeed, here’s what helped me most:

• Know your projects inside out
• Brush up on statistical intuition
• Practice building small ML components from scratch
• Think aloud — communication counts more than you think
• Don’t ignore fundamentals just because you’re used to libraries

If you are preparing for FAANG consider reading the below: Google Interview Experience

Apple Interview experience

ML/DS Interview Questions Dump

If you need help in interview prep/resume review feel free to connect with me here.