Where and How to Recruit Great Data Analysts

I’ve previously shared advice on when start-ups should bring on their first data analyst and how to evaluate candidates . The last step in hiring is finding and recruiting candidates.?

Before you can start recruiting, you need to figure out how senior of a data analyst you’re looking for. That’ll inform where you look for candidates.?

Hiring for experience vs. potential

One of the questions you have to answer when hiring your first data analyst is do you hire someone senior or junior? Do you hire for experience or horsepower? Do you hire a leader or someone who’s at the frontline and able to execute the analysis??

Senior hires can fail if they’re not doers

For the first data analyst at your company, your natural inclination is probably to bring in someone senior. They can come into your organization and hit the ground running. They’d be able to build out a data team later on. However, it takes longer to find a great senior analyst and they’re more expensive.?

The most common problem with hiring someone senior is if they’re not a doer. If they want to opine on data and tell people how to use data but not do it themselves, that’s definitely the wrong first analytics hire.

I went from running a team of 50 at Microsoft to being a team of one when I joined Zenefits. So I was a very senior hire and there were plans to grow out a team under me. But in the meantime, I was the one writing queries, summarizing the data, and building out reports.?

So it’s possible to hire someone too senior and you end up not getting a great ROI on that person. With that said, a lot of people in our field take a lot of pride in being able to maintain our skills. I rarely encounter somebody I’ve hired that’s too senior and has lost the skills required to do the work.

Also, beware of the winner’s curse when hiring people who have a lot of experience. Folks who have easily observable traits and experiences, like people who worked at Google as a data analyst, don’t necessarily work out when coming to a small start-up. There can be something that didn’t work out at their last job or a skill gap that’s making them want to take on a similar role at a smaller, earlier-stage company. Sometimes the reason for them joining is because the opportunity is exceptional. But very often, sorting by observables is a problem because you’re more likely to hire the wrong person .?

Junior hires can have high ROI

There can be huge returns when you hire someone junior who’s hungry to learn. Unlike other disciplines like sales or engineering, a lot of the best practices in data can be picked up pretty quickly. There’s less specialized knowledge, and the majority of what analysts do is critical thinking, consuming data, and figuring out causality.

I’ve found very junior analysts can deliver at very senior levels quickly because they’re essentially applying the same lessons they’ve learned to their work. If you’re able to get the right junior hire and quickly level them up , there’s huge ROI.?

Another reason junior analysts can quickly succeed is data analysis is often independent of context.?Let’s say you’re the CEO of KFC and you’re thinking about hiring a data analyst from Taco Bell. This is a natural hire because it’s somebody moving from one context to the same context, but that’s the wrong way to think about it. Experience within context isn’t necessarily that important.?

Instead, you’d want to hire someone who has problem-solving experience at a large B2C company. The size of the team, the size of the data, and their experience with those things are what are pertinent. But also I think those things are trumped by whether or not someone has the hunger and aptitude for the role.

The downside to hiring someone junior is you’ll probably need to hire someone above them eventually, or that person is going to have to learn from you when you don’t necessarily have relevant expertise.?

(If you’re finding this helpful, download Mozart Data’s guide to hiring your first data analyst for start-ups. You’ll find more advice to help you make the right hire, including interview questions to ask. Download it here .)

Finding the obvious and, more importantly, the not-so-obvious candidates

When looking for candidates, you can bucket them into a few categories.?

First, there are data analysts who have done the job before. They’ve had the title of data analyst, data scientist, or analytics engineer. They’ve worked in big tech before. For the most part, it’s only a new phenomenon that smaller companies are hiring data roles. So if you’ve had a data role, it’s typically been at a larger tech company — whether that’s at a late-stage start-up or at a famous big tech company.?

There are also people who haven’t had the data analyst title before but their skillset is similar to that of an analyst. These are people with adjacent careers, like folks in business operations, consultants, and those with strong quantitative backgrounds. They’ve worked with R or Python to do some modeling. They’ve worked a lot with Excel or Google Sheets. They’re very data-driven in their past roles or they’ve been the go-to data person at their company.?

Finally, there are folks who are coming from a totally different perspective or coming from school. They’re inexperienced. Maybe they’ve done a bootcamp, or they come from academic training and they’re passionate about analytical work. Many of the ones coming from school have had adjacent training. For example, in hard sciences or social sciences, there’s a lot of analytical rigor brought to problems. I joined tech after completing my PhD in economics, where I did a lot of statistical work and thinking about causal relationships. While I didn’t have explicit training in how to write SQL queries, I did have a lot of training in how to manipulate and clean data sets. While my work was in economics specifically, I was able to translate those skills into what would be helpful for a B2C company in technology.?

Because the data field often has a supply and demand imbalance, it’s challenging to find great people. By looking for folks who fall into the second or third categories, you can find great analysts who might get overlooked by most.?

Where to find potential candidates

Aside from the usual spots, like LinkedIn and Indeed, there are many more specific places to find candidates.?

There’s been a huge rise in Slack communities, email lists, analytics job hiring sites, and data and analytics bootcamps. Locally Optimistic is a Slack community where a lot of people in the data field hang out. There are also product-based Slack communities, like dbt .?

If you’re hiring an analytics leader, look at these communities and your network. In general, great analysts have followed other great people around Silicon Valley. They’ve been on great teams, so look for people who have been at big tech companies — those companies typically invest a lot in analytics. They’ve risen through the ranks and after a certain amount of time, maybe they’re ready to join something smaller.?

For junior candidates, there are a ton of great schools where you can recruit top talent. You can also look at data bootcamps for people who are early in their careers.?

Good luck with hiring and remember that as a start-up, you’re in a position to offer something unique that can help you hire a great data analyst. You’re able to provide an exciting and key role filled with learning opportunities and a seat at the table — something they can’t necessarily get by joining a big tech company.?

If you’ve hired data analysts before, share your tips and advice in the comments to help others out.