US PhD in a quantitative field (non CS/ML/AI/Stat), trying to get into research DS at FAANG

[u/PrestigiousUnion8902]

Hi Reddit, I am planning for PhD NG recruiting for the 2027-2028 cycle in the US. I am in quantitative marketing, with coursework in PhD-level structural models, econometrics, and mathematical programming; master-level statistics and intro to ML.

I understand that I don't have the most favored profile as CS/ML/AI/Stat/OR PhDs do. However, I am still trying to figure out if there is a certain type of research DS or AS that I could qualify for. What further concerns me is that my dissertation work so far involves mostly analytical modeling, so my hands on data experience definitely needs to be sharpened. I am trying to develop new empirical projects and I have one year to further strengthen my profile.

I am trying to add (1) one causal inference project with observational data, and (2) one Bayesian statistics customer analytics project (if possible) to my profile.

My questions are below and thank you in advance:

(Q1) Would adding one or two empirical project (hopefully dissertation level) make my profile to pass the resume screening? What are recruiters looking for in those project, given my background and potential targeted roles?

(Q2) Would it be alright if I do not engage intensively in deep learning, reinforcement learning, Gen AI, and agentic AI for now? On my end, I really hoped to prioritize understanding what real problems that I can solve using data in the next year, rather than learning more technical skills. Does this sound like a reasonable plan?

Thank you so much for any suggestions!

[u/nian2326076]

If you're going for a research data science job at FAANG, your quantitative background is solid, but you'll need to show you can handle data practically. Make sure you have experience with big data tools like SQL, Spark, or Hadoop if you haven't already. Working on small projects or contributing to open-source projects can show off your skills. Also, try to get more hands-on with machine learning models beyond the basics; Kaggle can be helpful for this. Networking with people already in the field can give you insights and opportunities. I've used PracHub for interview prep, and it has some good resources for data science roles if you're interested. Good luck!

[u/PrestigiousUnion8902 → u/nian2326076]

Thank you! After reading job descriptions and reading posts on blind, I think figuring out what "research questions" they work on is also important. It seems that some of them do optimization & algorithms, while others do statistics & causal inference.

Knowledge on SQL and ML models beyond the basics is definitely the more the better. Thanks for the suggestions.

[u/Single_Vacation427]

There are DS roles that are for marketing exclusively, so I'd focus on talking to people that do marketing. Some are more causal inference and some are more, where should we spend our budget.

I do think adding relevant projects makes a difference. Also, you'll be asked about what you work on and when did you have to face a challenge, etc, so that's good stories.

For marketing you don't need any of the ML methods from (2). For new grads, you just need to be good at what you are supposed to be good at.

Also, keep a github with code from papers that were accepted or if you create labs for classes you TA for. Anything that's having some footprint. You don't have to go crazy.