Hi folks,

This is the weekly digest for my blog, Irrational Exuberance. Reach out with thoughts on Twitter at @lethain, or reply to this email.

Posts from this week:

- Things I learned hiring a data science leader.
- Dipping toes in angel investing.

Things I learned hiring a data science leader.

I’m ending the year by pushing a handful of drafts that have something useful but that I’ll probably never finish. I’ve dubbed this draft week, and this is one of them!

Earlier this year I spent time designing, running, and tweaking the hiring process for a Head of Data Science. While I’ve spent a lot of time thinking about structuring and sizing engineering teams, I’ve come to appreciate that effective approaches are fact-specific. I wanted to avoid assuming that what I knew about engineering would apply directly to data science, which in turn shaped my approach to the hiring process as a learning exercise to better understand data science leadership in tandem with building confidence in our hiring decision.

Throughout the process I learned more about the data science function than I had in the preceding decade, and I wanted to write down what I learned, particularly as it relates to the problem of hiring a data science leader. That said, I’ll be explicit in caveating that this is a topic where I still have a lot left to learn, and I imagine I’ve missed some nuances and am wrong on some aspects.

Classifying leaders

Based on my experience chatting with about fifty data science leaders throughout the search, I’ve come to believe you can classify leaders based on three core beliefs:

1. Do they view the role of analyst as a peer role to data scientists? We wanted someone who viewed analysts are a peer role with a different skill set, not as a secondary or unnecessary role
2. Is the ideal data science staffing model embedded, centralized, or a hybrid? We wanted someone who was open to all these staffing models but had a strong perspective on why a given model would work for a given situation
3. Should data engineering be in the same organization as data science? We wanted someone who was “data science first” rather than a more generalized leader who stretched their attention over both fields

If you wanted to add one more dimension, you could potentially classify folks based on whether they believe machine learning engineering should be in the same organization as data science, although I found it to be a fairly uniform belief within the candidate pool I chatted with that it should be in the same organization.

Once you’ve classified the leader using the above features, you probably have a good sense of whether they’re a good candidate for your particular role from a belief’s perspective. For example, I was looking for someone who: \

• Valued analysts as peers with different expertise rather than viewing them as a secondary role
• Focused on matching the decision to use embedded, centralized or hybrid model to the circumstances rather than believing exclusively in any model
• Was passionate about data science informing product direction, rather than someone with a more generalized skillset who felt strongly about also leading data engineering

With those search criteria, candidates were able to understand what we were looking for and decide if it matched their interests. At this point we hadn’t filtered for their capabilities, if we moved forward we were confident that the work would be a match with their approach. More specifically, this happened in the initial phone screen that I conducted.

Evaluating candidates

The foundation of the interview for folks who decided to move forward with us past the initial phone screen was:

• Digging into technical dimensions of data science work were designed and run directly by our data science team,
• we had them lead a presentation with our executive team on their approach to starting at the company, they interviewed with a non-engineer stakeholder they’d work with closely (who varied a bit depending on calendars, but potentially a leader from our user acquisition or product management teams),
• we discussed people management (hiring, growth, performance management, and so on)

Beyond that core of evaluation, I spent time with the candidates digging in on a few additional dimensions:

• Creating non-linear impact. The impact of a core function like data science shouldn’t be primarily limited by how many folks are in the function, instead it should be derived from the highest impact projects we deliver. How do they identify high impact projects? How do they prevent recurring, routine work from crowding those high impact projects out?
• Sizing team. How do they think about sizing data science teams? How would they know that a team is too large? Too small? A ratio-driven approach is a fine starting point, but want to push a layer deeper to understand how specifics of data science function at a given company impacts that ratio
• Centralized learning. How do they support shared learning to avoid isolated work within the team? How do they support continued learning within the team?
• Seniority mix and hiring. How do they think about the mix of seniority within their team? How do they support growth for senior hires? How do they create an onramp for less experienced hires? How would you hire folks at those various seniority levels?
• What would they focus on in their first ninety days? Typical question, but a useful one to understand their thought process and openness to understanding the current context before trying to change things

Pulling that all together and we had a pretty straightforward process ensuring that folks understand the sort of data science leader we were looking for, and ensuring they were likely to succeed at our company. Certainly nothing life changing in these notes, but hopefully interesting for someone else conducting a similar search.

Dipping toes in angel investing.

I’m ending the year by pushing a handful of drafts that have something useful but that I’ll probably never finish. I’ve dubbed this draft week, and this is one of them!

As I get a bit deeper into my career, I’ve gotten a bit more focused on exploring the professional side-projects folks take on. In the past few years, I’ve spent time getting a sense for book writing, conference speaking, and podcast speaking. Some of those I’ve enjoyed (book writing), others I’m not planning to do much more of (conference speaking), and over the past year I’ve been exploring angel investing a bit more.

I started by reading what’s out there, including Angel Investing: Start to Finish (Wallin, Baltaxe), Angel: How to Invest in Technology Startups (Calacanis), and Fool’s Gold?: The Truth Behind Angel Investing in America (Shane). I also started a Twitter thread asking about what motivated folks to angel invest, and there were a bunch of great comments, especially those by Cristina Cordova, Joshua Schachter, and Mayank Verma. The cited motivations fell cleanly into those listed in Fool’s Gold?:

1. To make money
2. To get involved with private companies
3. To learn new things
4. As a hobby job
5. To find a job
6. To help the community
7. Because a friend has a business

The two that I’m personally interested in are (3) learning new things and (6) helping the community. In particular, my professional hobbies like writing center on the idea of nudging the industry towards what I believe is a more effective and inclusive version of itself, and my underlying question is whether angel investing is a more useful pursuit towards that end than writing another book or publishing more blog posts.

Working with my wife, we put together a budget for angel investing in 2021 and 2022, which I’m excited to use to answer that question. That budget is roughly enough for four investments each year. For 2021, I’ve made a particular emphasis on investing into different sorts of companies and opportunities (pre-seed, seed, a small fund, etc).

Over the course of the year, we did make the planned four investments, writing $25,000 checks, and the learning has begun! From these early learnings, we’ll refine our investment hypothesis a bit as we go into 2022. I’m still not quite sure what the refinements will be, but a few ideas: more focused on areas I understand (infrastructure engineering, etc), early stage but about to hire (I think my experience is most useful here), and founders less centralized in Silicon Valley (these folks have relatively less access to investment, and helpful to learn about the recent hypothesis that much of the next round of innovation will be away from current innovation centers).

My best bet is that I’ll really enjoy exploring angel investing as a hobby for a few years and then refocus my increasingly narrow time allocated to professional hobbies back to writing, but excited to see if I change my mind along the way.

That's all for now! Hope to hear your thoughts on Twitter at @lethain!