How I Hire

Ok I got you on the nerdbait title, now I got to quickly assert my ethos and show I’m not just totally full of shit.

Over the years in my current position(s), I’ve looked at probably 300–600 R&D applications to Driveline Baseball, probably given out 50–100 interviews, hired 10–12 people…and managed to distill (for myself) most candidates into ‘definite no’, ‘maybe’, ‘definite reach out/potential candidate’ in roughly 90–120 seconds.

Now that sounds kinda dehumanizing. And it is! Of course it is…I’m looking at something people have poured hours into (well not everyone I hope…judging by the quality of some answers haha) and have to sort them immediately. But I mean, there’s got to be a system in place. And I don’t use ALTIS or any similar AI/ML-assisted tool. It’s all me (and often one of my colleagues at Driveline — historically, Dan Aucoin and I have probably joint interviewed or at the very least joint researched likely 80% of shared applicants) so I have to come up with systems. Here’s my best effort at distilling what I do, why I do it and what biases I think this leaves open. Hopefully, this helps some people in similar positions — and/or even more hopefully this helps me by someone else volunteering valuable feedback.

The application/interview process for positions I’ve been in charge of (or joint in charge of) for hiring typically revolves around this general process. Will break up the rest of the piece with discrete examples, what I’m thinking and looking for at each stage

2. IF after filtering through the application pool above leaves us with many clumped, similar tier applicants then:

3. Likely a second interview — can take the shape of a group interview or a prepared case study type interview

Now what do I care about? Specifically in the first big hump aka going from #1 to #2:

I’ll open an app, throw the resume in one tab, scan through the app for any open source links or references to past (technical) work and throw that (usually github) link in a separate browser if so, and read the more critical thinking-intensive short form questions on the app.

Resume wise, I’m looking for discrete evidence that you’ve done projects or work that overlap with the job specifications. Pretty straightforward but bears specifying. If your resume doesn’t tell me anything valuable then it’s not gonna boost your shot.

I need to see evidence that you can code. Even before going to the code challenge portion of the interview. There are too many qualified applicants + I frankly have too many different hats to wear for me to give the benefit of the doubt to any interesting looking candidate (I’m not a hiring manager — hiring is one of about five or six core features of my job along with general leadership, strategy and vision, admin department stuff, project management and individual contributions across models, databases/pipelines and controlled studies/investigations). For most of the R&D applicants I’m looking at I need to at least see some breadth/depth in Python or R…either in the form of open source code or the output of a developed tool/report/app that speaks to some skill and ability.

I want to see that you’re not just a smart, technical applicant that’s shooting a random shot at this app. I want to see some depth and creativity in how you approach the ‘big data q’ on the app; what you can signal to me in the rest of the q&a that you actually do have some domain knowledge, that you have either tried playing around with some real, relevant data, that you know more baseball knowledge than just ‘oh yea I like fangraphs. Oh yea I’ve read some stuff on twitter.’ Figure out a way to get some tangibles in there. Drop discrete article references and be ready to talk about them in the interview. Having proven domain knowledge will go a long way towards making you an extra desirable candidate and have you stand out from the rest of the pool.

Also to top it off if you say something fresh, unique or just plain different that I haven’t seen before…I’ll admit I’m a sucker for that. I will entertain anyone/anything with enough variance to speak to possible high upside.

In interviews, I’m usually pretty loose, ask a lot of unplanned questions, and am happy to deep dive on anything. If you get nervous or feel you underperform during interviews, take some solace in the fact that I’ve hired multiple people that also have (underperformed in an interview). I don’t really care how well you interview — I come into the interview usually looking to tell if the candidate is an ok fit chemistry wise in the team and that they can talk about their work enough to pass the final bullshit meter, not for them to wow me with the smoothest answers or a micro second response. At the end of the day, I need to have confidence that whomever I hire is going to be able to execute on tasks and projects in a very fast-moving agile environment and that I can work with them successfully on a communication front. If I walk away from the interview with those concerns assuage, that’s a successful interview.

What Biases Does This Leave Me Open Too

2. Charismatic mf’ers

3. Idea-heavy, substance-light peeps

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