{ "media_type": "text", "post_content": "Haven't prepared for a technical interview in some time. Any suggestions? Role is data science consulting", "post_id": "5fb6a9ff64e17a0024b3d7a7", "reply_count": 24, "vote_count": 2, "bowl_id": "5ace5097be6e660013abcc42", "bowl_name": "Data & Analytics Consultants" }

Haven't prepared for a technical interview in some time. Any suggestions? Role is data science consulting

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Can’t BS your way through a data science interview if it’s with a legitimate company and if it is a legitimate role. If you BS your way through, I guarantee you the role doesn’t really require a data science SME consultant or otherwise to begin with. I’ve done BCG, FAANG and Finance interviews before. Know supervised vs unsupervised difference. Know the difference ML use cases in business context, like when to use clustering, linear regression, nlp, etc. What to do when target variable is binary vs continuous. Know general hyperparameters of algorithms to know high level what changes do what to the algorithm and result.

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Agree with this, with the one exception that if someone doesn’t know this stuff they don’t have any business interviewing for a DS role. Not sure if I just expected industry would ask more, but if you actually have DS experience this stuff should be a cakewalk (and you should have experience to dive much deeper into at least one niche DS topic you’ve worked on).

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U should also know how to do distributed training and

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Retraining

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L1 vs L2 regularization

Just bullshit your way through the interviews in the case studies stuff

I’ll answer A1 questions from before because those are questions I also asked both before and after I joined McK : A) I can’t speak to how product people are perceived by traditional consultants but DS have a great perception because you would only get staffed on analytics studies so YOU are THE one that gets stuff done. You don’t just give material to some random BA so no issue there. It’s not as SWE in finance for example who always feel like tools for the quants. B) - Exit ops : early tenure, you can go to GAFAM for data scientist roles that are becoming more popular. At 4/5 YOE, you would have demonstrated ability to lead junior colleagues and you would be a lead DS / PM in tech companies. Some of our DS also apply for CTO / Chief scientist roles at some startups from what I’ve seen C) I think that depends on how you frame it and what role you’re applying to. If you don’t want to be a DS anymore, you’ll focus on how you derived insights and how it led to impact. If you want to remain technical, you’ll focus on what you implemented in short engagements. All in all it can be a really great role if you want to get exposure, esp. early on in your career, to industries and people in high positions. But I’m not gonna lie to you, the bad WLB is getting the better of me especially as we’re not even traveling anymore. If I find a good exit in finance or tech, I’ll jump ship !

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