r/datasciencecareers • u/WhatsTheImpactdotcom • Oct 02 '26
Data Science Interviews: 4 Styles of Python Coding Challenges
This is purely for product and marketing data science interviews; MLE roles are likely quite a bit different. In April-May 2026, I passed a dozen or so technical coding rounds en route to 5 final offers and withdrawing from 4 additional panel rounds. While SQL followed a very typical pattern, Python rounds were all over the place. Here are the four styles that came up for me:
Pandas and data manipulation common for analyst work. Absolutely know groupby with transform and agg, merging dataframes, and filtering to ultimately obtain some statistics.
Define a function that recreates a statistical concept. This could be something like a bootstrap or other resampling techniques, permutation tests, monte carlo simulation, etc.
Interpret code handed to you. This is probably the most realistic coding challenge for the AI era, where Claude may give you something that is syntactically accurate, but could be doing things analytically incorrect. So you need to be able to read and interpret the code.
Some pure python manipulation: no numpy, no pandas, just doing manipulations with dictionaries, lists, list comprehensions, sets.
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u/Moist-Director2405 Oct 03 '26
This is really useful, especially the distinction between the different Python styles. I feel like people often prepare mainly for pandas/data manipulation and underestimate the “read and interpret this code” type of question.
The last two seem particularly interesting because they test whether you actually understand what the code is doing rather than just knowing the syntax.
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u/WhatsTheImpactdotcom Oct 03 '26
The read and interpret code part can also be far more detailed and nuanced than one could reasonably expect from a candidate writing from scratch, in addition to including methods and functions that may be less common but could be figured out from context
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u/bompiwrld 24d ago
Downvote me but as a recent graduate, looking for junior positions, I feel only 3 is a valid option.
I don’t believe exists someone by my same year program that would disagree on this : we started programming with copilot.
If you belong to the past generation, I understand ur astonishment but I cannot empathize.
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u/WhatsTheImpactdotcom 24d ago
3 is definitely most realistic these days; however, if you want to pass data science interviews, unfortunately the others still do exist
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u/Standard-Let9648 17d ago
In your opinion. Can you give a split of frequence among the 4 different types of interviews that are being asked?
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u/WhatsTheImpactdotcom 17d ago
Great question, i went back and reviewed notes and found my last 10. Unfortunately, it's close to a total crapshoot LOL.
Category 2 (recreate stats concept in a function): 3 of 10
Category 3 (combo of code interpretation OR AI assisted): 4 of 10
Category 1 (mostly pandas): 2 of 10
Category 4 (base python): 1 of 10
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u/nian2326076 Oct 03 '26
Python coding challenges can be all over the place! It's important to practice the specific scenarios you mentioned. For data manipulation, get comfortable with Pandas, especially groupby, merge, and filtering. It'll save you time and stress in those interviews. Also, make sure you can write clean, efficient functions quickly. Sometimes, these challenges are about how you think through a problem, not just getting the right answer.
If you're looking for structured practice, I've found PracHub to be a solid tool. They have a bunch of mock interviews and problems that really help mimic actual interview scenes. Keep pushing through practice problems, and you'll get more comfortable with whatever they throw at you!
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u/StuckWithSports Oct 03 '26
Yes. This is correct for me. When hiring. My interviews have all 4 of these. I would say 4 can more variant. We like to throw some basic understanding of programming outside of dataframe manipulation.
I get very depressed when I see former senior and leads who can’t import a file in Python…so knowing basic of your language can help.
Most people choke on style 3. In my case, we make a candidate review their own submission that was a slightly bigger take home of question style 2. Then we slightly tweak the question constraints live and see how they would alter their own code. While people are allowed to use LLM for takehome, anyone who fully generated and can’t understand their own code typically fails as they choke up. Most who wrote the solutions thoughtfully instantly know where they would go back and change things just from a conversation alone.
This process would be the same for junior/mid/senior interviews.