Should You Hire a Data Scientist?

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Due to the explosion of data we’ve been talking about so much lately, many marketing teams (70% according to NetVantage) are considering adding a Data Scientist to their teams. While this might sound like a made up title, it’s very real, and according to many marketers, it’s a very valuable position.

So what is a data scientist, exactly?

The Harvard Business Review explains their qualities as follows:

“Data scientists don’t do well on a short leash. They should have the freedom to experiment and explore possibilities. That said, they need close relationships with the rest of the business. The most important ties for them to forge are with executives in charge of products and services rather than with people overseeing business functions. As the story of Jonathan Goldman illustrates, their greatest opportunity to add value is not in creating reports or presentations for senior executives but in innovating with customer-facing products and processes.”

Want a more concrete description? Econsultancy says Data Scientists:

  • “ May be involved in the design and development of systems that collect and process large amounts of data using tools like Hadoop and programming languages like R
  • Need to have a deep understanding of statistics and probability
  • Are capable of designing and testing predictive models
  • Provide the greatest value by answering the questions ‘Where are we likely going?’ and ‘What would we need to do to go somewhere else?’
  • Will realistically need to acquire a high level of domain expertise  ”

Unsurprisingly, good data scientists are not easy to find, but by most accounts, they’re worth it.

However, answering the question of whether you should add a data scientist to your team requires more than a simple analysis of the value this role could bring to your team. It also requires an honest examination of your budget and the training and resources you could offer someone in this new role.

Are you considering adding a data scientist to your team? Tell us why (or why not) in the comments.

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