I’m allowed to use AI in my interviews??
The new era of Data Science interviews
We already saw this happening in 2025, and now more companies are doing the same in 2026. More and more companies are allowing the use of AI during interviews.
But here’s the crazy part.. You’re not just allowed to use AI in your interviews. You’re not just expected to use it. You are evaluated on how you use it.
Which makes sense. Many companies are mandating their employees use AI tools to help them with their jobs because of the efficiency gains. So naturally, they would want to hire employees who already know how to use AI to make themselves better.
So how do these AI-enabled interviews work?
First of all, you should expect that the problems are going to be harder, because AI assistance is assumed.
There are 2 setups for these types of interviews:
Some companies are open to you using any AI tool available out there. You can use ChatGPT, Claude Code, Cursor.. whatever your choice of AI is.
Some companies have integrated AI assistance into the interview coding tools, like Coder Pad. In which case, you might not get as high-powered AI as you are used to.
How are you evaluated on the AI-enabled interviews?
Like all other Data Science interviews, these AI-enabled interviews are not just about getting to the right answer.
You are evaluated on
Whether you know when and how to use AI
How you break down complex requirements
Whether you make sound technical & tactical decisions with AI
Whether you can catch and fix issues
How you interpret the output and come up next steps
Yup, it’s very much about your process, and how you communicate throughout the process.
The skills they are testing for: critical thinking and communication.
How do I ace AI-enabled interview?
Tip #1: Don’t outsource your thinking; outsource the work.
AI can do the mundane, repeatable work for you, like writing code. That’s what AI is good at.
But you need to own breaking down the problem and architecting the solution. This is where you as a human excel: you have context on the business, the team, and the problem. You can think beyond the surface level.
By the way, need more context on the business or the problem? Ask the interviewer. This demonstrates curiosity and depth of thought.
Tip #2: Read every line the AI gives you.
A key part of the interview evaluation is how you evaluate the AI. Yeah, you’re being evaluated on how you evaluate. What in the inception.
But seriously, the interviewer is watching to see if you blindly accept the AI’s output or if you are thinking critically about it. So that means read every line. If something looks off, call it out. If the AI made an assumption that doesn’t match the problem, flag it to the interviewer and then to the AI.
Need even more tactical tips. Ask yourself these questions:
Does this make sense for this business?
Is the AI missing any important context?
Are there any edge cases that we have not considered?
How would the end user actually experience this? Would this solution work for them in practice?
Tip #3: Iterate.
Don’t feel like you have to one-shot the answer with AI. In fact, trying to get the perfect answer in one prompt is a red flag to interviewers. It signals that you don’t understand the real-world limitations of AI.
The best approach is to start by breaking down the problem into smaller steps first. Then, tackle each step one at a time with the AI.
For example, let’s say you’re asked to build a churn prediction model. Instead of writing one massive prompt, break it up:
First, ask the AI to do some basic exploratory analysis on the data
Then, have it build a simple baseline model
Next, evaluate the results together and identify areas for improvement
Finally, iterate on the model with more features or a different approach
At the end of each step, check the AI’s work, give feedback and iterate.
Tip #4: Know when you should step in.
Sometimes writing five lines yourself is faster and cleaner than three rounds of prompt refinement, and recognizing that tradeoff in real time shows maturity.
Just because the AI is there, doesn’t mean it has to do all the work for you.
Tip #5: You own the interpretation and the recommendations.
A key part of Data Science is not just getting to an answer, but interpreting what it means and deciding what to do next. This is the part that AI cannot do for you. AI can run the analysis, but it doesn’t know your business, your stakeholders, or what decisions are on the table.
So once you have your results, take a step back and explain what you’re seeing. Connect the output to the original problem. If the model shows that users who don’t log in for 7+ days are likely to churn, say that. Then go further: recommend an action. Maybe it’s a re-engagement email campaign at day 5, or maybe it’s flagging these users for the customer success team.
You want to show that you’re not just a technical executor. You’re someone who can take data and turn it into a STRATEGY. That’s the difference between a good candidate and a great one.
So start practicing now. Use AI tools in your day-to-day work, get comfortable with the back-and-forth, and build the habit of thinking critically about the output.
By the time you walk into an AI-enabled interview, it should feel like just another day on the job.
ICYMI (in case you missed it)
Your white collar job might be in danger.. how not to paniac
How to get set up Claude Code
5 hacks to level up your SQL skills
12 guided projects to get started with AI Engineering