What are the challenges of building a data team at a startup? originally appeared on Quora - the knowledge sharing network where compelling questions are answered by people with unique insights.
Building a good data team is a challenge anywhere, but there are some questions that startups in particular have to consider:
- Goals: Why do you need a data team? Are there concrete goals for them to work towards, or do you want to appease your investors? What do you expect them to do in a year?
- Roles and team composition: What role should I hire for, given my goals? Data engineer, data scientist, analyst, data communicator? What is my eventual team composition that I'm building towards? What level of seniority are you targeting?
- Team lead: Do I have a strong team lead who can identify, attract and recruit great data scientists, and who can work with the founders to define the company's data strategy? Expect them to spend 50% of the time on recruiting and interviewing, at least initially.
- Timing: When should I hire my first data scientist? Is there enough work for them if I don't have a ton of data yet? But if I don't have a data scientist, how do I know what data to collect, how, and why?
- Org: Where will the data team fit in your organization? The answer will depend on your answers to the questions above.
- Data culture: Is your company ready to treat data as a first-class citizen?
- Infrastructure: Can data scientists do their job as soon as you hire them, or do they have to set up your data infrastructure first? This is not a problem in itself, unless there's an expectation mismatch or you're hiring for the wrong role / in the wrong order.
- Recruiting and retention: Data scientists are in demand. What do you offer that others can't? Unique learning opportunities, unique data, social mission, career growth? Do not bait and switch -- everybody's going to have a bad time if you misrepresent the kind of work they're going to do at offer time. This is much easier if you thought about 1-7.
A typical bad scenario: Founders know they need a "data play" (or worse, "AI play"); investors and clients keep asking about machine learning (or worse, deep learning). Founder hires a machine learning scientist, often a fresh graduate, and tells them they will build machine learning models. Scientist gets there -- there's no infrastructure, no ETL, data is a mess because nobody tried to do anything with it yet, instrumentation is non-existent except for ops, everything takes forever, engineers are working on their sprints and their patience is wearing thin. Everybody is frustrated: Scientist because they can't do the job they were hired for, engineers because they have to take time away from their work to do "grunt work" and pull data from prod for the scientist who sits around and does nothing useful, founders because the data scientist didn't even produce a decent dashboard, much less this magic machine learning everybody keeps raving about. Scientist quits and startup got "burned"; next scientist (if any) inherits culture wars and skepticism.
This won't happen to your startup, though, because you thought through the questions above ahead of time.
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