Building a Data Science Team from Scratch

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recipeby Angela Guess

Ashu Dubey, co-founder of 12 Labs, recently wrote in Forbes, “There are two main approaches for how you can spice up your current projects with data science. One is to license data science technology from another company. The problem with this approach is that AI technology has not sufficiently matured to be generic enough for broad use, though there are a few startups doing great work in this field. The second approach is what we adopted at 12 Labs. It was to build an in-house data science team. When we started, we didn’t have any data scientists. But within a year, we built several data science products that even established companies in the fitness space have not been able to build. Here are the steps we took to build our from-scratch data science team.”

Dubey goes on, “(1) Find an engineer who is a hustler with a good product sense. Aspiring product managers in your team might be a good fit, as frequently such people work on developing a good product sense while they are still working as an engineer. (2) Hire someone with a statistics background. I don’t mean a data scientist. You need a pure statistician because your engineer will need pointers from someone who is strongly grounded in statistics and machine learning. As I understand it, even a strong data scientist can’t tell you which approach or algorithm is the right one without trying several approaches first. So, if you have someone willing to try out several approaches (in this case, the engineer above), the statistician can point them in the right direction.”

Read more here.

photo credit: Flickr/ Muffet

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