Knowing When Your Startup Should Hire A Data Scientist

August 5, 2016

A Data Scientist has the ability to detect and measure outcomes from iterative changes made in a service or product, which allows startups to adapt and evolve effectively. However knowing when to hire a Data Scientist can be tricky. Hire too early and they’ll be under-stimulated, under-appreciated, and could end up quitting. Hire too late and not only are they left with a messy and high pressured job, you missed the boat on making key advancements in your business.

At The Data Incubator we work with hundreds of startups who are looking to make that first hire or grow their existing data science team. For the startups looking for an initial hire, knowing when is by far their most pressing question. Here’s what we tell them.

When To Make The Hire

At it’s core, data science helps businesses make decisions on product and operating metrics. Business intelligence and analyst functions can also do this, albeit with a more basic toolset, so it’s worth exploring whether you need a Data Scientist just yet. To leverage a Data Scientist 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?

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

At the conception of your startup you need to assess how your data will be captured, time period requirements for storage, attributes generated, and end performance benchmarks for risk management and the customer experience.

To hire effectively, learn 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, the major role of the Data Scientist 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.

Image Credit: Data

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