Building A Successful Data Science Team

Companies looking to start a data science team often get overwhelmed with the challenges and specific characteristics of hiring, building and growing a team. Also, starting a team of this kind is not the same as it is with an average software development team.

Therefore it’s necessary to review going beyond definitions of the “unicorn” data scientists and looking at what happens in real-life teams where data scientists work.

What the industry calls a 'data scientist' now is really several different roles. The term data scientist is tossed around loosely these days, so much so that it's creating a bit of confusion in the tech industry.

When people say there's a shortage of data scientists, they mean there is a shortage of people with all of these different skills.

Subdivides the data scientist role into three distinct jobs, each requiring a different skill set: business analyst, machine learning expert, and data engineer.

You need these three groups of people to work together in order to inform the business decision-makers, 

The role of business analyst existed long before the terms "big data" or "data scientist" were in vogue. This person works with front-end tools, meaning those closest to the organization's core business or function, such as Microsoft Excel, Tableau Software's visualization tools, or QlikTech's QlikView BI apps. A business analyst might also have sufficient programming skills to code up dashboards, and have some familiarity with SQL and NoSQL.

The recent hype surrounding big data, however, has led many business analysts to rebrand themselves as data scientists even though they are not.The second data science role is that of machine-learning expert, a statistics-minded person who builds data models and makes sure the information they provide is accurate, easy to understand, and unbiased. 

"These are the people who develop algorithms and crunch numbers,

 "They are interested in building models that predict something."

A machine-learning expert, for instance, might develop algorithms that predict consumer sentiment or estimate a person's influence in a particular industry.

"There are even machine-learning algorithms that look at images and tag them automatically, or look at videos and try to understand what the video is about,"

Like the business analyst, the machine-learning expert isn't a new profession, but rather one that's existed in the last 30 years or so. 

The third key job, data engineer, is the foundation; they are the ones who play with Hadoop, MapReduce, HBase, and Cassandra. These are people interested in capturing, storing, and processing this data… so that the algorithm people can build models and derive insights from it." 

However, it's nearly impossible to find one person -- that data scientist unicorn -- who excels in each of these three areas, and that's why organizations must focus instead on building a data science team. So don’t  try to find one superhuman who does it all. For Sure you need three experts: business analyst, machine learning expert, and data engineer