SWE vs DS in Mckinsey QuantumBlack/BCG Gamma
Which has a better salary, easier to get into and has better exit opportunities, for an ML Engineer with 1 year of exp. I would argue: Salary and exit opportunities: DS Middleway salary and exit opportunies: ML Eng Easier to get into: SWE and Data Engineer YoE: 1/2 TC: 🥜 45k in Europe
Given the current market DS may be easier to get into but long term I think Swe or MLE will be better for getting new opportunities and career growth within tech. Can’t say for the others but quantumblack DS interview is a coding/DS assessment and a few round of case study based DS questions. No leetcode from what I experienced. Entry level with them was about 160k base and level up was 200k
Correction, DS is easier for consulting by companies not for tech companies. If anything DS is really hard for tech companies now because they already have high headcount and you really don’t need a large portion of DS compared to other positions like SWE
Stupidest thing I’ve read today, congratulations
Current DS Consultant at McK, I applied at QB and for a DS role on consulting side and ended up on DS side. I would say they are very similar, overall. (agree with Dropbox^)in that QB is probably more marketable for tech so it depends what exit opps you are interested in. In my work I have been able to leverage the ML/DS systems that are already in place. My guess would be that QB starts out paid a bit more (my guess is 150 vs 160-175) but the upward trajectory/earning potential is higher for client facing consultants vs. middle/back office Data/ML guy, so once again it depends.
Former Gamma here. In consulting DS is better because you get to do most of the interesting ML work while MLE have to do ops and infra work which oftentimes sucks. But the drawback is that the MLE title is much more marketable and useful when recruiting for tech. Ideally you’d be an MLE who does mostly modeling and less ops.
Thank you for you answer, what about the interview process ? Is it leetcode based for MLE and DS ?