Hire better data scientists: A field guide for hiring managers new to data science
By: Eli Goldberg
Tags: Hiring Tips, Machine Learning, Interview, Data Science, Manager

Step 1. HR Screen.

All candidates start out with an HR screen to collect basic information. Between the HR screen and Step 2, all candidates get a short ‘mission flyer’. This flyer is, essentially, an extension of the job description. It dives a bit deeper into the mission and helps candidates overcome the question of ‘why us’?

Controlling for my institution, and this pandemic, ~65% of all resumes are filtered out at this step.

Step 2. Technical interview. I have a lot of thoughts (and guidance) on how to craft a technical interview for data scientists (see below). However, everyone must pass a technical interview. That’s not to say that everyone should have the same level of difficulty — it should be matched to the candidates level and ‘sphere’. n.b., I use ‘sphere’ to describe the ‘flavor’ of data scientist I’m looking for e.g., consultatory, heavy research and development, a clinical data expert, etc.

I run a large clinical analytics team so my ‘sphere’s and leveling are overlapping. A ‘P5’ is essentially a principal data scientist. My expectation is that senior members of the team accumulate skills from other spheres as they advance (or are more senior hires because they already possess them).

Depending on the quality of the HR screening, and once again controlling for things, ~15% of my candidates are filtered out at this step. For my own technical interviews, I’ve observed that only ~25% of candidates that I’ve interviewed have passed their technical interview.

Step 3. Make a go-no go decision. Good candidates do not struggle with the technical interview. If you’re on the fence, it’s a no. Your team’s time is too valuable to put someone in the process that you don’t think will make it. Also, don’t forget about the ‘soft’ skills, as well. Do control for introversion, and don’t be unnecessarily harsh, but confidence, poise, brevity, and clarity of communication are important.

Step 4. Review the technical interview notes to confirm candidate level and ‘sphere’. Prior to inviting candidates for an onsite interview, make sure to review the level with respect to the strength of their technical interview. In contrast to the guidance above, it’s possible that you could have ‘mis-leveled’ someone. As a consequence, they could have gotten a much harder technical interview. If you have a more junior level role open, it may be worth considering putting them in the process, only after clearly communicating to the candidate that you’re modifying their expected role.



It is rare, but sometimes the technical interview is so impressive that you may be tempted to ‘bump up’ the candidates level. Don’t. On the margin, you want folks who cost one level and ‘act’ another level. This leads to more rapid advancement, which keeps people happy.

Step 5. Provide a level and ‘sphere’ matched interview panel. If you’re hiring a senior data scientists, make sure to setup the onsite interview to showcase the strength of the team and the vision of the company as a whole. Having a diverse team, including non data scientists, will help to sell the big impact and vision. I have a lot of thoughts here on how to make the best out of a candidates ‘day of’, but these will be embedded in the next article, specifically.

Step 6. Review the onsite interview data and make a go/no-go decision. After all is said and done, you (the hiring manager) need to decide. Remember, people will forgive you for passing on a ‘good candidate’, but hate you for hiring a ‘bad candidate’. The contrapositive is fickle beast.