"Data Scientist in Residence", "Chief Data Officer", "Big data engineer"...the data science buzzword du jour is “Data Scientist.” These data scientists are part data analyst, part statistician and part software developer. With data quickly becoming a top strategic business priority for many companies, the need for data scientists will only increase – especially as we move toward an ever more data-driven world.
If data represents a strategic asset for your company, then you should be wondering if a data science team is needed and is it better to buy or rent the talent? Data analysis can be done by anyone with analytical skills, right? Well, the functions of a data scientist on a data science team are broader than initially meets the eye….and they typically need to work closely with data analysts and subject matter experts.
Data Scientists are in demand, and the salaries for US-based professionals can easily reach six-figure levels (about $120K + benefits according to Indeed). The number of data science jobs is expected to grow by 24% over the next five years. But do you really need a team of data scientists?
The data science team doesn't have the same structure that data analysts do – they are more fluid in that data scientists can operate at any level of the organization and perform specific functions depending on their skills. However, there are some basic responsibilities data scientists have:
This variety in skills is a big reason why data science teams are so popular among companies. Specialty roles can be filled by outside consultants to quickly fill a skills void with experienced talent. Filling in data and analytics fluency gaps with specialists on a temporary basis is less expensive than hiring employees in the short term. This also means that these Data Scientists are more effectively utilized as they're not restricted to performing just one type of task. The downside is that over the long run "renting" data science skills is more more expensive than building in-house capabilities.
One of the biggest benefits of data science teams is their breadth and depth in data skills. They're more capable than an analyst since they have other skills to fall back on like software development, domain expertise or data infrastructure. This makes them better equipped for solving data analysis problems compared to a smaller team with only analysts.
These are just some of the main responsibilities data scientists have on data science teams. There are other skills they can bring depending on their depth of experience and specific skillsets. For example the data science team can support organizational efforts related to strategy developing, analytics interpretation, technical product management and more. Having basic background knowledge about what a Data Scientist does in general will help you better understand your options when it comes to filling positions with a heavy data analysis component.