As A Startup, When Do I Hire A Data Scientist?

Knowing when to hire a Data Scientist can be tricky. Data Scientists have the ability to detect and measure outcomes from iterative changes made in a service or product, which allows startups to adapt and evolve effectively.

They’re a great asset to your team. But if you hire too early they’re likely to be under-stimulated, under-appreciated, and could even quit. Hire too late and you’ve missed the boat on making key advancements in your business, as well as leaving the new hire with a messy and high pressured job.

We work with hundreds of startups at The Data Incubator, all looking to make their first hire or grow their existing data science team. For the startups looking to make an initial hire, knowing when is their most pressing question. Here’s our advice.

When To Make The Hire

To put it simply, data science helps businesses make decisions on product and operating metrics. With a more basic toolset business intelligence and analyst, functions can also do this, though not to the same degree. Because of this, it’s worth exploring whether you need a Data Scientist just yet.

To leverage a Data Scientist in your team appropriately, you need a basic volume of events and historical data for them to provide meaningful insights on.

What Kind Of Startups Need A Data Scientist?

Firstly, at the beginning of your startup journey, you must assess how your data will be captured, the time period requirements for storage attributes generated, and end performance benchmarks for risk management and the customer experience.

Startups related to cloud-based or mobile offerings are likely to need at least one data scientist from the get-go, while others should hire a Data Scientist when they have enough historical data, a volume of events, and problems data science can solve.

We recommend learning what hiring managers don’t understand about hiring a data scientist, so you don’t make those same mistakes. A Data Scientist can seem like a magician at times, and while you can expect them to fix problems to some extent, don’t hand them a complete mess. The more scattered and overlapping your systems are, the less valuable the data becomes for large-scale analysis.

Remember, a Data Scientists main role is to effectively communicate insights from your data with the rest of your largely non-technical team, so your business can move forward in leaps and bounds.

Guest post by Catherine Moolenschot Marketing Associate at The Data Incubator

The Data Incubator is an 8-week fellowship training PhDs to become data scientists based in NYC, DC, and SF. The fellowship selects 2% of its 2000+ quarterly applicants and is free for fellows. Hiring companies (including EBay, Capital One, Palantir, Pfizer) pay a recruiting fee only if they successfully hire. You can read about us on Harvard Business Review, VentureBeat,WSJ, or the FT, or read about our alumni at LinkedIn, Palantir, Amazon or the NYTimes