What IT Managers Need to Know about Data Science

A few months after you hire a data scientist or engineer for your team, they come to you and give you their two-week notice. They tell you that they received a better offer from a more prominent company. You accept the notice and wonder why this happened.

Statistics on data scientists retention show that data wranglers, business analysts, and report developers stay at their current jobs for one to two years. These professionals receive messages from external recruiters about new job opportunities once a week. You can better the odds of these folks sticking around if you tweak your management style a little. Here are some ideas you can employ.

You are sitting in your office when one of the company's data scientists comes in and tells you they're quitting. As your (now) former analyst walks out the door, you think, “that’s third data hire who quit after just a few months.” "How can we retain more of the talent we hire?"

IT managers like yourself are well aware of the tight data science labor market. A narrow talent market increases pressure on you to retain the talent you currently have. There is a myriad of reasons why these people leave, and you can not control for every reason. However, here are some things you can do before hiring new talent that will go a long way towards retaining qualified data scientists.

As the IT manager at your company, you understand how vital analytics has become to its success. However, you might struggle to meet demand for analytics products for your organization.

As a leader in your company, you understand the importance of data to building robust customer experiences and having efficient operations. However, many companies collect lots of data but are unable to get the reports and insights to make that data actionable.

The volumes and sources of data are snowballing. To support this avalanche of data are a growing number of applications and tools for analytics and data science.