0
$\begingroup$

I would like to check/ask if there is anything particular that I need to prepare for data scientist interviews. I am quite unsure/lost about the requirements since I am coming from academia (see my background in the next paragraph), and am also working on a tight timeline. I am hoping to transition into an industry data science role -- the aim is to leave academia for good. So in essence, I am asking: What is one or two thing I can learn up on before applying for data science jobs in industry (I'm looking into banks and government areas like defense etc). (Maybe learning SQL?) Thank you for the advice. It would also help me if you can link/refer me to the resource that you are talking about.

Remark. Regarding timeline, I have around a few months worth of time where I can allocate 1 to 2 days every week to learning. So I think this is only enough time to pick up 1 or 2 things, but I think this is better than not doing any preparation.

My background. I am currently a final year PhD student in machine learning where my research is on machine learning theory (more specifically, in the field of high dimensional inference), so there is some coding and a lot of designing and analyzing algorithms (i.e., math). I have never interned or worked in the industry before (went directly into my PhD program after my undergrad where I majored in both computer science and statistics). So I am familiar with the following things:

  • Taken a few machine learning courses throughout my education (both undergrad and graduate courses). I've published around 10 papers in machine learning theory (in high dimensional statistics), but those are quite mathematical.
  • For my PhD, I've written code to run simulations for my algorithms using python, numpy, and plot them using matplotlib. I've also used the machine learning package scipy and the optimization package cvx. I have experience with R in my undergrad but that is a long time ago.
  • In my spare working time, I've read 'An Introduction to Statistical Learning' to gain more breadth on machine learning algorithms, from a practical point of view.
  • All my internship experiences were as a research assistant in my undergrad university, where I did theoretical research on randomized algorithms/machine learning.

As you can tell, this will be my first time coming out of academia so it is quite scary for me but I am sure of my decision to leave after my PhD -- and hence am looking for some guidance. Thank you so much for the help.

Improve this question
$\endgroup$
1
  • 1
    $\begingroup$ Math/Stats is probably ok then, but I would certainly play it up during interviews. Upload some code to github, so that you can point at it. Definitely SQL. Play with some cloud platforms, e.g. try spinning up a lambda function on AWS, experience with something like BigQuery or Snowflake would be nice. Something from deep learning libraries. I tend to use JAX (used TensorFlow before), but PyTorch may be another good choice. Timeseries might be a good thing to check, for banks, Containers, Docker and ways to serve models via REST APIs $\endgroup$
    – Cryo
    Commented Dec 23, 2023 at 10:04

0

Reset to default

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.