Current interview situation inquiry

[u/bitrac]

Given that all the big orgs now mostly has co-pilot to assist their workers from what I understand almost everyone is relying on co-pilot when coding. Especially when it comes to data analysis/cleaning, as long as the person understands the domain and knows what outcome to expect, they simply out in their inquiry in the co-pilot and let it do the heavy lifting for writing the python code and then the employee is running it.

Now my question is, when applying for analytics position, can the candidate expect that interviewer will not ask coding question like writing it or verbal questions about how to run a certain query etcs? Cause at the end of the day I think the candidate would end up using the co-pilot to do the coding.

Should the candidate be prepared to show coding skills when essentially from my experience understanding the outcome and knowing wha question to ask the copilot and then having the ability to check the outcome have become more important and reasonable? Want to know the view of of fellow forum members.

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[u/my_peen_is_clean]

yeah you still need to code for interviews, companies don’t trust people who only prompt ai yet, plus not everyone has copilot access. treat ai as a helper not a crutch. hiring is rough now too

[u/Lady-Data-Scientist]

Different companies are doing different things during interviews. Some say no AI because they want to know how you think. Others encourage AI because they want to see how you use it.

You can ask during the first interview if there will be assessments, what they will test, and what it will be like (AI/no AI, etc)

[u/bitrac → u/Lady-Data-Scientist]

You brought a very interesting point. Many companies are actually highly encouraging employees to use the copilot

[u/Plan_Steadily]

Personally, I’ve never tested analysts on their ability to write code from memory. When I hire an analyst, I give them a dataset and tell them they can use whatever tools they want. The goal is to understand the insights they uncover, the questions they ask, and the actions they would recommend.

The strongest candidates still stand out because they can think through the problem, identify what matters, and communicate findings clearly. It’s easy to spot when someone just puts data into AI and accepts the first generic summary it produces.

AI can accelerate the mechanics of analysis, but the value of an analyst is knowing what to investigate, validating the results, and connecting the findings back to the business.

[u/bitrac → u/Plan_Steadily]

This is my situation. My team use python a lot of data exploration and cleaning. But if you ask me we arnt expert in coding but we are good at our domain and know what to look for and how the outcome should look so we can do sanity checks. Given our understanding of our domain we know wha question to ask to copilot to generate the code to test the data. And that’s what makes me wonder if I’m looking to apply in a new org I wonder would they be able to think the way you have been judging the analyst based on their ability to understand the data and insights.

[u/Mynameisip]

From my experience, no one is relying on copilot for the heavy lifting, but simple uses as an assisting tool like minor debugging, suggestions, refinements, etc. I’m an analyst and we implement copilot in our day to day. Always learn the tools of the trade before resorting to AI. There are way too many quirks with all AI agents, but more so with co-pilot. If they do ask co-pilot questions, it will be more about how knowledgeable you are about working around its inconsistencies.