Shift Interviews From Memorized Code to Real-World AI Problem Solving

In today’s rapidly evolving tech landscape, the relevance of traditional coding interviews is increasingly questioned.

April 20, 20263 min readData Science

The interview process is stuck in a time capsule, and it's failing everyone involved. The focus on memorized data structures, algorithms, and complex SQL or pandas puzzles ignores how software development actually happens today. When most candidates are using AI to assist with code, asking someone to invert a binary tree from memory doesn't measure problem-solving ability. It measures recall under pressure, a skill that's becoming less relevant by the quarter.

What companies should be evaluating is real-world problem-solving, the kind that involves talking through an ML concept, designing a solution, or even completing a coding exercise where AI is allowed. That's how people work on the job. You sit with a problem, you consult your tools, and you iterate. The exercise should mirror that reality, not an artificial memory test. If a candidate can explain why a model is underperforming or how they'd approach a messy dataset, that tells you more about their capability than whether they can write a recursive function from scratch.

The persistence of these older methods isn't about rigor. It's about convenience and inertia. LeetCode-style questions are easy to administer and grade, but they filter for preparation, not potential. They favor candidates who have time to grind through hundreds of problems, which often correlates with privilege rather than skill. That's a shallow way to build a team, and it pushes away thoughtful engineers who might excel at the collaborative, AI-assisted work that defines modern development.

For you, the candidate, this is a signal. If a company is still leaning on outdated evaluation methods, ask yourself what else they're missing. Are they adapting to new workflows? Are they open to how AI has changed the craft? The companies that move toward practical, scenario-based interviews will find better fits and more capable hires. The ones that don't will keep wondering why their interview loop doesn't translate to on-the-job success. The shift isn't coming. It's already here, and it's time for hiring practices to catch up.

From Data Science

I don’t really get why interviews are still so focused on obscure data structures, algorithms, or complex SQL and pandas problems. At this point, most of us are using AI in some capacity to write or assist with code anyway.
Why does it still matter if I can invert a binary tree in 10 minutes from memory? Wouldn’t it make more sense to talk about actual experience, ML concepts, or even do a coding exercise where AI is allowed, like how people actually work on the job?

Read the original at Data Science

Briefing

Source: Data Science
Published: April 20, 2026

How Big and Small Teams Measure Your Potential Differently
My interview experience has been a massively varied at this point, but what I've noticed is the massive difference between big companies like FAANG and smaller orgs like DS in banking or random small companies At FAANG it's kind of like an IQ + knowledge test (what google calls Role related knowledge) and smaller companies do assessments for very specific types of modeling or use cases, like build a model being evaluated on a certain metric. So at FAANG I was asked questions like "why is the formula for s.d. different for pop. vs sample', or 'what happens to the bias/variance in x,y,z situation' mean while at companies that are smaller and pay less they sent me a random 30-60 minute assessment and asked me to directly clean data and code up a model with sklearn/pandas. Is this what everyone else has experienced? It does seem like at smaller or traditional companies test if you will be a good code monkey while others look for actual understanding. submitted by /u/LeaguePrototype [link] [comments]
Explore how AI fits into your data workflow without the overwhelm.
DS sometimes feels like there's infinite amount of things to learn. Most recent trend has been AI engineering And it's not like AI came in so you can deprioritize something else, but instead it just gets added to the heap. So you already had this massive amount of content to know from stats & product, trad. ML, deployment, ops, engineering, cloud, etc. and then you add the new thing on and the new thing. And when you read the job descriptions they literally list of all of this. I just had an interview for a random gaming company that wanted cloud, snowflake, stats, ML, ops, and AI experience in 1 person and it was for like 3-5 years of experience. And I wish that this was a one off thing but it seems to get more common. It actually feels like FAANG is easier to interview for because they silo people and not expect you to know and do everything What is your strategy for learning these skills without getting exhausted, or do you feel companies expectations are overflated? Is this a by product of AI where people are expected to do a lot more with less? submitted by /u/LeaguePrototype [link] [comments]
How AI is reshaping data science hiring toward real-world judgment
Been noticing new DS hiring products like Litmetrics.ai lately, which seems much more focused on real datasets and messy business cases than the classic coding-test format. A lot of DS work today are more like to be end-to-end analytical judgment with AI in the loop. That feels like a different hiring target than the classic CodeSignal / HackerRank screening - pretty sure most DS have used them in interviews. Curious what other people think. Is DS hiring actually changing on the assessment layer - to whether candidates can work through an real business problem, or putting AI language on top of the classic coding test & screening process is still the best way? submitted by /u/Alarming-Wish207 [link] [comments]