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How to pick a data scientist, the right way

EnterpriseGuides
by Paul Cooper
, 11 Feb 2014Guides
How to pick a data scientist, the right way

Data analytics is big right now. Big data is big right now. There's a wonderful buzz going on around big data, and buzz generates venture capital, and venture capital generates new technologies and tools. For the enterprise, harnessing the power of big data could mean getting a better grasp on your customers and their habits, and could make the difference between sinking and swimming in an increasingly data-driven world.

So you want to set up a data science team – and who doesn't? You've sent out your job ads, and the CVs have come flooding in. So how do you make that all-important choice: who your new data scientist is going to be?

1. Understand the skills required

A data scientist is someone who understands the domains of programming, machine learning, data mining, statistics, and hacking - in a good way: knowing how to get in and grab the data they need.

A good data scientist needs to understand his domain, whether it's science, engineering or business. They need to be able to cut through the myths associated with big data, but most of all, a data scientist needs to be able to tell a story. They need to be able to teach the data to tell a story you didn't know from data we already head. They have to be someone who likes numbers and likes people.

2. Make an informed choice

Applications will usually come in three categories: the business expert, the data analytics guru, and the IT professional.

As a business, you have to choose which aspect you want to prioritise, and what's important for the data you're dealing with. You have to take a low-hanging fruit mentality for data. That means you find data that's possible to extract, and that has value to someone in your organisation.

It could also be beneficial to hire someone with experience in startups. A candidate with startup experience can help an enterprise tap into all of the fast-moving advances and information coming from the outside - because often in business we become so insular. When we're successful, we think we've done big data, and we know it all.

3. Get ready to hire more

The data is only getting bigger, and it's getting more and more important for businesses to stay ahead. The future will involve many data scientists, each with their own domain. For instance, MGM recently hired a HR data scientist, for example. While a single data scientist might suffice for an SMB, one is absolutely not enough for a large enterprise going into future dominated by the Internet of things and ever-increasing floods of information.

The important thing to remember is that a data scientist is a consultant role, and no one person ever becomes an expert. The data scientist ties everything together, maybe, but he's an expert in none of it.

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