The 5 Most Common Interview Mistakes

I was an interviewer at Amazon, Meta, and other tech companies. I’ve also spent the last year coaching dozens of clients who landed their dream Data jobs.

After seeing 100+ interviews up close, some clear patterns have emerged. So, without further ado, here are the top 5 most common interview mistakes… and more importantly how to fix them.

#1 Underselling yourself

Oh man, this is by far the most common mistake I see. Candidates unintentionally downplay their experience through their choice of words.

• ❌ "I think my work was good enough to get promoted."

• ❌ "I just built a simple regression model for this."

• ❌ "I basically developed a data pipeline."

Stop that! Own your accomplishments. An interview is NOT the time to be humble.

• ✅ You did something impactful.

• ✅ Your work was complex and valuable.

• ✅ Your success was earned through skill and effort.

How you talk about your work determines how it’s perceived. Be PROUD of what you've done — and let that show in your interviews.

Try this: Record yourself answering this question — "What is one professional accomplishment you're most proud of?" Then listen back to the recording. Are you underselling yourself? Have you used any words that discount your accomplishments?

#2 Not being structured in your approach

A lot of people don’t realize this: most companies explicitly rate communication as part of the interview rubric.

As a start, here are 3 easy ways to sound more structured:

1. Use numbered lists — “I have three ideas...”

2. Group by category — “The metrics fall into four buckets...”

3. Try a pros and cons list — “There are trade-offs with these approaches…”

#3 Saying everything rather than the most important things

You might think the more you say, the more knowledgeable you sound. But actually… it often makes you come across as inexperienced and unfocused (see point #2 above).

An example from my interviewer days: When I ask an A/B testing question — and a candidate launches into every single step and detail of running an experiment.

Nooooo..

The assumption is: we already know you can run an A/B test. Use the question to show depth and expertise. For example, you want to talk through:

• Nuances and gotchas in this specific setup

• Metric tradeoffs and why they matter

• Suggestions to improve experimentation infrastructure

#4 Not being prepared with questions

At the end of most interviews, your interviewer will give you a chance to ask them questions. This is your chance to figure out if the company is actually the right fit for you.

It’s also your chance to stand out from other candidates by asking thoughtful, relevant questions. Don’t ask surface-level stuff like:

• ❌ "How do you like working here?"

• ❌ "What’s the company culture like?"

• ❌ "What tech stack do you use?"

These are boring, generic, and honestly, don’t give you much insight into the company.

Instead, come with questions specific to the team, the challenges, and the business. Here is a short list to get you started:

• ✅ "What’s the biggest data challenge your team is facing right now?"

• ✅ "What’s one question you wish a Data Scientist could answer for you today?"

• ✅ "Can you share an example of how data influenced a company strategy decision?"

#5 Not asking for help when you get stuck

Asking for help is not a weakness. It shows humility. It shows collaboration. And in a real job — not just the interview — these are critical skills to have!

Not sure how to ask for help gracefully? Try this:

“I’m a little stuck. I think this is the direction I want to go — does that sound right to you?”

You’re not throwing up your hands and giving up. You’re demonstrating that you’re keen on trying, but you just need a nudge in the right direction.

These mistakes are easy to fix! The next time you practice for an interview (or in a real one), try to pay attention to these common pitfalls. You got this!

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