I talked to two DS managers the past two days, one at C3.AI, an enterprise AI company and the other at BlockFi, a crypto exchange company.

I've been asking questions like:

• what kinds of projects you're working on?
• what is the culture like?
• interview process?
• what made you hire your last team member?
• what specific skills you look for?
• what would impress you about a new-grad candidate?
• what kinds of projects would impress you?
• what do you enjoy about company X?

Here were a few things took away from the chats (from my goldfish memory)

• it's difficult to hire gen AI talent, people's experience are not directly relevant since it's a new field, mostly toy projects and nothing substantial
• a diverse team should have research people and builders
• technical experience is the baseline, if you can't pass the python, sql, stats and prob, and ML basics, don't even think about getting into FAANG (side note: paraphrasing here, but this made me slightly stressed about interviews even thought i haven't started my program yet and I'm still in Malaysia)
• in your internship, if you're not doing what you like, create your own projects if you can, focus on delivering results that you can show for, for your next job (i.e. if you're doing analytics, but you want to do ML)
• first job should matter, it's the foundation for career, but don't be afraid to experiment and explore if you don't know what your niche is
• two step process for DS job / interview
  • step 1: find a fit, there's ML core, product, full stack DS, etc., customize your resume for the role, further customize for company if you really like the company (highlight experiences and projects)
  • step 2: skills are python (up to lc medium), sql (do all LC Qs), get a cheat sheet for stats and prob, they like asking about hypothesis testing and p-value, ML algorithms like bagging and boosting, and specific domains (NLP, CV) study the algorithms (transformers, CNNs, etc.)
• highlight your contribution with your work experience, and if it lacks technical (i.e. you worked on analytics but you want to pivot to ML), use projects and focus on technical aspects
• there's offense vs defense analytics, offense is about growing the product, defense is fraud and risk
• lots of candidates can't pass python medium questions
• for projects like forecasting, accuracy doesn't have to the main goal, instead of using more advanced methods, what's more important is using a simple solution and being able to explain how you obtained that number; how well you're able to tell a story and having evidence and data to back it up.
• junior DS struggle initially with red-tape involved in working at a company, lack of documentation and having to ask around for help, and spending at least 6 months understanding the data
• for resume projects, focus on telling a story, talk about why you chose this project, what you learned about the data, why you chose that methodology, have a short executive summary, include charts and graphs. less on the technical, more on the result