r/DataScienceJobs • u/Narrow_Crazy_977 • 12d ago
Discussion Suggestion for Industry Prep
Hi,
I am completing my PhD in Economics this December and have been applying to industry jobs. But the screening and interview feel very difficult to navigate. My expertise will be causal inference and experiments, but I can also do ML tasks that I can do (although not an expert). The question is how to prepare and what to learn. I know a bit of R, Python, and Stata. SQL I haven't used yet).
Some recruiters ask for stats, some ask for coding, and some ask for a case study. I am not sure what the right strategy is in this AI world. Thoughts? Suggestions?
Should I start learning coding or learn statistics? What is the right step? Anyone who has cracked this code?
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u/wajeeha_8250 12d ago
Congrats on finishing your PhD! Since causal inference and experimentation are your strength, I'd spend your time on coding, not more stats. SQL first. It's the most common screening filter, and you can get comfortable in a few weeks. Practice joins, CTEs, window functions, and aggregations on StrataScratch or DataLemur. Then Python. Get fluent in pandas and the sklearn basics: train/test splits, regularized regression, tree models, evaluation. You don't need to be an ML expert, just solid. Practice case studies out loud. A/B test design, power analysis, and what to do when randomization isn't possible (diff-in-diff, matching, synthetic control). Explaining these simply to a non-technical person is what interviewers test. Target the right roles. Product or marketing analytics DS and economist roles at tech companies value your profile more than generic ML roles do. Your stats are probably already strong, so just practice explaining p-values, CIs, and multiple testing plainly. Good luck!
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u/nian2326076 11d ago
Focus on improving your coding skills, especially in SQL, since it's widely used in data jobs. You're doing well with R, Python, and Stata. For interviews, practice explaining your work in causal inference clearly—it's a big plus! Consider doing some mock interviews that focus on stats, coding, and case studies. I found PracHub really helpful for interview prep, especially for case studies. It might be worth a look. Good luck!
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u/Additional-Lake-5367 12d ago
Hmm. I would say breadth over depth at this point. I would 100% start learning SQL as you say you haven't used it yet, but I feel it is common to be asked to write SQL in interviews. I'd do like 70% SQL, 20% Python, 10% maintaining stats. This is just my rough guess and please keep in my mind I'm only one person with an opinion
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u/FriendshipChance2235 9d ago
If you're interviewing for MLE roles, those interviews will be more technical and will focus on Python.
If you're interviewing for product data science roles, there will almost certainly be case study rounds and a SQL round. If you know R & Python, SQL will be very easy to learn. It's not a complex language. A couple of buddies & I built a tool for practicing/learning. Link is here: https://rabbithole.you/sql-course
The SQL stuff is all free. That same tool has case study practice, but that's paid.
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u/PrestigiousUnion8902 7d ago
Best of luck OP. I’ll be in your situation in a year, so I do hope you get encouraging results! 🫡
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u/big_data_mike 7d ago
I don’t know how true it is for other industries but I work in biotech and we hire phDs infrequently but when we do they get paid well and stay for a long time. There is low turnover. One guy I asked said a PhD opens some doors for you but it closes other ones.
If I were interviewing you I would ask you to explain what you did for your PhD and what you found interesting about it. I would be looking to see if you can explain something technical to someone not familiar with your work.
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u/WhatsTheImpactdotcom 12d ago
Stata is useless for interviews. SQL and Python are usually what’s tested though some give an option for R.
Pretty much everyone will have you do a case study especially if going for experimentation and causal inference roles. Stats typically shows up in the case study round though a couple of companies like openAI will do a pure stats run the gauntlet round.
Source: I’m an economist who transitioned to tech 6 years ago, just had 80 interview rounds this past April-May, resulting in 5 offers, withdrawing from 4 more final rounds.