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12 August 2022


The Great Data Scientist Exodus is Impacting Your Organization Now: Here's How to Stop It

Employer demand for data scientists is at an all-time high—and it’s only expected to grow. The US Bureau of Labor Statistics predicts the number of jobs requiring data science skills will increase by nearly 30% by 2026.  

At the same time, only 2% of current data scientists have not left their jobs in the last five years. This current of deep dissatisfaction running through the data science community is a phenomenon we’re calling The Great Data Scientist Exodus.  

Whether you’ve been grappling with turnover for months (or even years!), or if this is news to you, we can guarantee the Exodus has influenced your enterprise.  

The good news? We’re here to help. In this post, we’ll walk you through why your data scientists are quitting and how to keep them from walking right out the door.  

5 Things Causing The Great Data Science Exodus & How to Prevent Them  

On the surface, the answer is pretty straightforward—right now, lots of data scientists find that their day-to-day job isn’t living up to the expectation they had of being a data scientist.  

Instead of creating valuable models, many data scientists are faced with unfulfilling career paths filled with long hours doing tedious work, inadequate tools, and a lack of trust in their work throughout the organization.  

We’ve heard stories from data scientists that faced frustrating work environments with little-to-no support for their projects—we narrowed the motivations behind the Exodus into the top five factors and compiled some concrete strategies you can use to retain your team’s data scientists.  

1. They spend most of their time doing grunt work, not creating value  

Data scientists are often presented with messy, unpredictable data and tasked with creating industry-shattering models with it—in record time. On top of that, strict governance standards force data scientists to jump through hoops before they can even start working on meaningful projects.  

Solution: Cut back on the tedious data prep  

With the right platform, anyone (whether you’re an analyst or an engineer) can clean large datasets in minutes so that expert data scientists can spend more time getting projects across the finish line.  

2. Their organizations aren’t giving them the tools they need  

Sending code via email with no version control, presenting data science output on a PowerPoint slide, and producing results in nonintuitive software is a recipe for dissatisfaction.  

Solution: Invest in solutions that enhance their productivity  

Support your data scientists with an end-to-end platform that streamlines, automates, augments, and enhances the entire data science lifecycle. Then, go one step further by implementing a contemporary, cloud-first platform that minimizes overhead and eliminates hardware requirement management.  

3. Employers haven’t figured out how to support their data scientists  

Data scientists need a job where they can contribute real value, but for many organizations, it’s not clear what that looks like. HR teams don’t have a career path outlined or a clear-cut map for how data scientists can best provide value, leaving them directionless.  

Solution: Give them the right training  

Show your data scientists that you can deliver a fulfilling career path and offer programs that help them do just that. Data science is an amalgamation of multiple disciplines to begin with, and data scientists tend to be intellectually curious at their core. Give them the opportunity to expand their broad set of tools and intellect.  

4. There’s a lack of organizational buy-in to AI  

85% of AI projects fail. Why? Because stakeholders don’t take the time to fully understand data scientists’ models, so they ultimately don’t believe in them. And if the work you’re doing every day isn’t supported by the rest of the organization, wouldn’t you think about quitting, too?  

Solution: Cultivate a data-driven culture throughout the organization  

Upskilling your employees involves bridging the gap between businesspeople and data people so that anyone in the organization can make an impact with machine learning. When the business and data experts in your organization understand each other’s work, you’ll create a more supportive culture—and get better results.  

5. They’re not happy with their compensation  

Amidst The Great Resignation, 79% of employees believe they can make more money switching jobs than staying put.  

Solution: Pay them competitively  

Compensation is the number one driver for employee retention. Do your research, understand what you need to offer to be competitive, and incentivize prospects to join and employees to stay by offering them a comprehensive package.  

Ready to Stop the Exodus?  

Make sure your data scientists have what they need for success—starting with a DSML platform that supports them fully.   

Check out our on-demand webinar, How the New RapidMiner Helps Data Scientists Be Even More Impactful, to learn how we help you bring more models from prototyping to production.

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