10 Tips To Keep Your Top-Performing Data Scientists

If you want to keep your top Data Scientists happy, they must get complete job satisfaction. It can be difficult when there is a shortage of talent in the industry, and Headhunters often approach these individuals with lucrative offers. In this blog post, we will discuss ten tips for keeping your best Data Scientists onboard and how to make them feel valued and engaged!

10 Tips To Keep Your Top-Performing Data Scientists

Money talks - While usually low down on a Data Scientist's list of drivers, it can be the deciding factor between two similar opportunities.

A good work-life balance - Data Scientists are often highly engaged and focused during their work which can be highly cerebral, so it's essential to have a healthy lifestyle outside of the office as well! A company that offers flexible time-off policies can help with this goal by allowing employees more flexibility in when they take their vacation days or personal leave.

Cutting edge technology - Data Scientists are always curious about what's coming next, so it is crucial for them that their company invests in these types of things as well!

The people - A good team dynamic with other Data Scientists on your project or within a department can make all the difference when you're working closely together day in day out.

Mentors or knowledge sharer - Good Data Scientists are hungry to learn and progress. A team full of intelligent people willing to share knowledge and help each other progress becomes a win-win for everyone.

Impact - Data Scientists often talk about their desire to make an impact in their role. Whether that be for society, the business they're working in, or something specific aligned with their interests. Using their skills to solve real-world problems is a prominent driving force for some.

Learning - Data Scientists are usually interested in learning new things and expanding their skillset, so make sure you're providing opportunities for them to do this regularly! The more they know, the better equipped your team will be when it comes to delivering value. Learning lunches, conferences, online courses, further education, or simply experimenting with some new tech are a few examples of stimulating a Data Scientist's desire for learning. Give them a choice; what would they like to invest their training budget on?

Freedom and autonomy to bring their ideas to life - One of my clients had a day each quarter when everyone came together and pitched ideas. The best ideas got backing, and the company ran with them. This initiative was an absolute hit with new starters and kept the best people there.

Don't build a team of cogs - The last thing you want is for your Data Scientists to feel like they're just a cog in the machine. It's crucial that their work has an impact on something, and it matters! This can be as simple as showing them how their work is benefiting the business and customers. I usually ask candidates about their achievements, which they love to talk about, but when I follow up to ask what quantifiable benefit this had for the business and customers, they often don't know.

Challenge - Data Scientists are intelligent people. They're used to being the smartest person in any room, and they like a challenge, so you must give them one! This can be as simple as setting stretch goals for their team or giving individual challenges on projects where there is no right answer but just different ways of approaching an issue.

In summary, if you keep your Data Scientists engaged, challenged, working with great people, well paid, learning, working with cutting edge tech, happy and making an impact in their role, they won't have much reason to look elsewhere. So not much then! But it's the price to pay for retaining top talent.

If you're a Data Science leader and offer half of this, let me know. I have talented candidates who would love to talk to you.

Note: While this blog is about what attracts and retains Data Scientists, there is much overlap when it comes to Data Engineers, Machine Learning Engineers, Research Scientists, AI and Deep Learning Specialists, Big Data Developers, DevOps Engineers and so forth.