Top 4 Reasons Data Scientists are Motivated to Change Jobs

October 1, 2018 | 4 minute read
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The US could have as many as 250,000 open data science jobs by 2024 (InfoWorld), and the data science skills gap will find companies scrambling to train or hire talent in the coming years. So, the war for data science talent is real.

Knowing what motivates a data scientist to change jobs can provide valuable insight on how you can attract data scientists as well as shed light on how to retain the data scientists you may already have on your team.

Over the course of speaking with hundreds of data science professionals, one of the first questions I like to ask is, “What is the motivating factor for you to change jobs?” The answers are always insightful and after years of interviewing countless job candidates, I’ve learned the reasons for changing jobs can vary greatly depending on market conditions.

With the candidate-driven market we are currently in, this information is especially relevant. To help you become an employer of choice amongst this most sought-after group, I have compiled a list of the top 4 reasons that motivate data scientists to change jobs.

1. There’s no infrastructure in place to effectively manage and support a successful data science initiative.

In this digital age, data science and advanced analytics have emerged as core business disruptors, so it’s not surprising that every company wants to be part of it. However, before you jump into hiring data scientists, make sure you are prepared. While there are many factors in implementing a successful data science and advanced analytics infrastructure, two crucial elements are at the forefront: whether you have the data needed to do the job properly, and whether you are current on the latest technologies. 

If you don’t have a clear outline of what you want the analytics to determine or know where to get the data, your data scientists will spend all of their time locating, cleaning, and compiling the data rather than finding value in the data and writing machine learning algorithms to drive insight. This a sure-fire way to ensure your data scientist will quickly become bored and start looking elsewhere.

It’s also very important to data scientists that they work with the most relevant technologies not only to keep their skills up-to-date, but also to keep things interesting for this inquisitive group of professionals. So, make sure you have the latest and greatest technologies and invest in the hardware to actually run the algorithms.

2. There’s no clear vision for the goal of the data science team.

Corporate growth is not enough information to launch a successful analytics initiative. Specifically, where do you want to see the growth and why? There are a lot of ways to impact the growth of an organization and creating a clear vision and a predetermined set of goals for the data science and analytics team is key. This is a crucial factor in motivating the team. Knowing how their work could affect the corporate mission and overall corporate growth provides a roadmap for the data scientist and makes them feel part of something larger, which is one of the biggest reasons data scientists choose this field in the first place. So make sure you conduct your due diligence, and outline a clear vision for the team to work towards.

3. There’s no proper hiring order established.

Many companies hire junior data scientists before they hire experienced senior data practitioners. It’s important for a company to first hire someone to set the analytics strategy and manage the data science practice. While titles vary across industries, your first hire (Chief Data Officer, VP of Analytics, etc.) should be someone who will develop an analytics roadmap and focus on data governance, documentation, as well as any legal issues that may arise. While there may be some overlap, there is a big difference between practicing data science and managing a data science initiative, and the relationship between the two is an important relationship that should be hired in specific order.

4. Expectation versus reality turns anticipation into disappointment.

Mismatched expectations are the most common reason why data scientists lose interest and become unhappy. Remember, data scientists choose this field because they want to use algorithms to make sense of complex problems and influence business direction. It’s important to have a clear vision for the data science team and offer exposure to cutting-edge technologies. However, be careful not to exaggerate the role in trying to attract top-tier data scientists, as they will quickly become disillusioned. To keep things interesting for the data science team, allow them to be exposed to different departments instead of keeping them working in their own little bubble so disappointment doesn't set in. This way, they can see firsthand how their efforts are influencing the growth of the organization.

At the end of the day, it really boils down to knowing that top data scientists enjoy a fun, stimulating, and rewarding environment that is well-thought-out and organized. It might sound like a lot of effort to attract and retain top data scientists, and, well, you are right. Just remember that a successful analytics operation will improve not only bottom-line, but also brand recognition, employee retention, and so much more. Whether you are trying to become an employer of choice to attract top data scientists, or retain the ones you already have in this very competitive market, make sure you know the top motivating factors for a data scientist to change jobs and avoid these pitfalls.

Happy networking!

Mary Ann Hunt

Mary Ann Hunt is a leading AI, data science, and advanced analytics executive search consultant with Quant Connection, a best in class executive search firm specializing in AI, ML data scientists, and advanced analytics professionals. She is a highly sought-after consultant advising clients on building and growing their AI and data science teams and has had great success in bringing together top minds in this field.

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