Coding Under Pressure Isn’t the Same as Coding at Work

Why even strong data scientists struggle in interviews, and how to build the coding and communication skills to get through them with confidence.

Jun 17, 2025

👋 Hey! This is Manisha Arora from PrepVector. Welcome to the Tech Growth Series, a newsletter that aims to bridge the gap between academic knowledge and practical aspects of data science. My goal is to simplify complicated data concepts, share my perspectives on the latest trends, and share my learnings from building and leading data teams.

“I write Python and SQL every day at work.
Why do I freeze in coding interviews?”

If this sounds familiar, you’re not alone.

Many data scientists struggle with interviews, not because they lack technical skills, but because they haven’t practiced those skills under pressure.

And that’s the real difference.

💻 Real-World Coding vs. Interview Coding

At work, the focus is on collaboration, context, and long-term value. In interviews, the goal is to see how you think, debug, and make decisions quickly.

At work, the focus is on collaboration, context, and long-term value.
In interviews, the goal is to see how you think, debug, and make decisions quickly.

🎯 The Skill of Interviewing

Here’s the thing most people don’t realize until it’s too late:

Interviewing isn’t just about what you know. It’s about what you can recall, structure, and articulate under pressure.

That’s a completely different skill set.

Let me give you an example:

You might know how to join 3 tables and write a window function. But can you do that while a timer is ticking, someone is watching, and you’re expected to explain why you chose one approach over another?

That gap is what trips up many talented data scientists. And unfortunately, the interview process is optimized to evaluate that narrow slice of performance, not your true long-term value.

So how do you bridge the gap? You train for it like a skill:

Interviewing = Problem Solving + Communication + Pressure Management

A good interview performance requires you to:

Frame the problem clearly. Can you restate the question? Can you identify missing details?
Pick a direction and justify it. Can you explain why you’re using a certain approach or structure?
Write clean, working code. Can you implement your logic quickly and debug it without getting stuck?
Communicate throughout. Are you thinking aloud? Are you guiding the interviewer through your reasoning?
Handle curveballs. What do you do when something unexpected comes up?

These aren’t things you “read about” and suddenly know how to do. You have to practice them deliberately — with feedback, reflection, and repetition.

Shameless plugs:

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Gain hands-on experience and build a portfolio of industry AI/ML projects. Scope ML Projects, get stakeholder buy-in, and execute the workflow from data exploration to model deployment. You will learn to use coding best practices to solve end-to-end AI and ML Projects to showcase to the employer or clients.

How to Practice the Right Way

A few strategies that make a real difference:

Mock Interviews > Passive Prep
You’ll learn far more from one mock interview with feedback than from reading five blog posts. Don’t just consume — simulate.
Explain Your Thinking as You Code
This feels awkward at first, but it forces clarity. Practicing this builds confidence and structure, even in ambiguous situations.
Review Your Own Solutions
Go back and ask yourself:
– Did I structure this well?
– Were there unnecessary steps?
– Could I explain this to someone else?
Track Patterns
Are you consistently stumbling on edge cases? Recursion? SQL joins? Start building drills around your weak spots.
Treat Interviews as Discussions, Not Auditions
You’re not performing. You’re collaborating. Bring the mindset of a teammate, not a test-taker.

Interviewing well doesn’t mean you’re a better data scientist. But it does mean you’ve trained for the format. If you want to change roles, grow into leadership, or just feel more confident in high-stakes settings — this is a muscle worth building.

And we’ll work on it, step-by-step, in the masterclass.

Benchmarks to Aim For

You don’t need to grind hundreds of Leetcode questions. But you do need consistency under pressure. Here are some useful benchmarks:

If you're consistently overshooting these, don't worry - that’s just a sign to build muscle memory and mental structure.

Final Thought

If you feel like interviews aren’t showcasing your real abilities, don’t get discouraged. Interviews test a different muscle. It takes practice - not just to solve problems, but to communicate your thinking and show structure under time pressure.

We’ll build that muscle together.

Upcoming Courses:

Product Data Science
Master Product Sense and AB Testing, and learn to use statistical methods to drive product growth. I focus on inculcating a problem-solving mindset, and application of data-driven strategies, including A/B Testing, ML, and Causal Inference, to drive product growth.