Computer on table, coding, data scientist

If you’re having trouble holding onto your valued data scientist employees, you’re not alone. Data scientists have one of the highest turnover rates in tech, remaining at their roles only 1.7 years on average.

With the demand for data scientists steeply increasing and a shortage of qualified candidates, it’s not easy for businesses to find replacements to hire. To make matters worse, poor employee retention can have a negative impact on morale, team culture, and productivity.

Let’s break down exactly why companies are facing challenges retaining their data scientists, and what they need to do to ensure their data scientist employees want to stick around for the long haul.

What is a data scientist?

Data scientists leverage mathematical, statistical and computational techniques to analyze data and solve business problems. They collect data from various sources and use techniques such as modeling and machine learning to extract patterns, insights, and meaningful conclusions that they can communicate to business leaders and stakeholders for improved decision-making.

Why are companies facing challenges retaining data scientists?

Data scientists can help companies gain game-changing insight into customer behavior, marketing and sales strategies, operational efficiency and more. With the speed of technological change accelerating and the rise of big data, the market for data scientists is highly competitive and data scientists face no shortage of opportunities to jump on.

Some reasons why a data scientist might leave your organization to join another include career advancement, higher compensation offerings, the opportunity to do more interesting work or use newer technologies, as well as burnout. But with the right strategies, it is more than possible to retain your data scientist employees — here’s how.

Tip #1: Hire the right fit

It’s important to understand whom you need to hire to ensure an opportunity will be a long term, mutual fit. Are you a lean startup that needs a generalist who can tackle pretty much anything, or a large company looking for a specialist to fill a niche role? How will a candidate fit into your organizational structure, and what are the opportunities for them to learn and grow on your team in six months versus two years? Answering these questions will help increase the likelihood of finding a good match for your team.

It’s also important for the engineering and recruiting team to align on the skills and role specialization you’re looking for when you hire a data scientist to optimize your targeting of candidates to interview.

Tip #2: Build Strong, Data-Driven Culture

What creates a sense of purpose when you show up to work? We all know that having a mission is key — whether it’s fighting fraud, optimizing cloud infra or revolutionizing healthcare. However, a communicative, data-driven culture is equally necessary to ensure that your data scientist employees feel that they are making an impact.

To do just that, it’s important to ensure that your company’s data is transparent to stakeholders and your data science team. You should be able to articulate how data analysis is well-integrated into meaningful conversations around business operations.

Tip #3: Ensure you’re compensating at market rates

Employee compensation is just one factor influencing a data scientist’s decision to stick with a job (or not). However, employees who are undercompensated often also feel undervalued and underappreciated. Ultimately, the cost of recruiting, interviewing and training a new hire is likely to significantly exceed the cost of simply offering your data scientist employees adequate compensation so that they don’t leave in the first place.

Tip #4: Offer opportunities for personal development

Learning and development stipends, career coaching, mentorship and continuing education are all great perks to formally offer. However, satisfying your data science talent’s desire to grow and learn new skills can be as simple as ensuring that they are working on innovative new projects and technologies. Data scientists who get to use cutting-edge tools and techniques to solve important business problems won’t be bored — and they’ll make a big impact on your company’s success as well.

Tip #5: Be conscious about fighting burnout

With data science skills highly in demand, companies may feel tempted to overload their data scientists with multiple projects, tight deadlines and long hours. However, a lack of work-life balance will quickly lead to burnout.

At a minimum, companies should offer flexible hours and ensure that employees take adequate PTO. They can also be intentional about combating burnout by regularly scheduling team-building activities, frequent employee recognition, and manager check-ins.

However, by supporting your data scientists and ensuring they have meaningful, innovative and purpose-driven work to tackle, you can ensure they thrive at your organization for many years to come.

About the author

Lauren Greer is a tech recruiting manager and content marketer at Celential.ai, an AI-powered, human-in-the-loop sourcing solution that builds strong pipelines of candidates ready for interviews cost-effectively. Prior to joining Celential.ai, Lauren was a researcher and award-winning instructor at UC Berkeley.