Industry Insights

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Why Are So Many Data Scientists & Data Engineers Leaving Their Jobs?

August 24, 2021

It’s not hard to see that the data science & analytics market is extremely hot right now, but now we also have the data to prove it! Our hiring surveys this year showed many teams are hiring, and, in addition to this data, we’ve been having countless conversations with hiring managers and HR leaders centered around high attrition, retention challenges, and an increasingly tough battle for data science, analytics, and data engineering talent. We’re also seeing an incredible number of data & technology professionals either actively or passively evaluating their job options.

So, why is this happening? Why are so many data professionals jumping into new roles right now?

1. After job searches and/or hiring was put on hold in 2020, it’s Game On for 2021

One of the trends we discussed in our recent salary study (which you can download for free here) is that there is a lot of pent-up demand in the hiring market in 2021. A lot of data scientists and data engineers were understandably nervous about making job changes during 2020, and we saw many put voluntary job searches on hold.In addition to expected hiring (adding headcount or backfilling roles), we’re also seeing some companies jump in and try to build teams from scratch or drastically increase their net new headcount, which will only exacerbate the supply/demand issue. The increase in professionals exploring their options, combined with so many teams hiring, is why our team spoke to InformationWeek in even more detail about this acceleration in the market right now.

2. Passive job seekers are getting more curious

Especially lately, many professionals are curious and interviewing opportunistically due to remote roles opening up, even if they’re not seriously considering a change. Unfortunately, on the hiring side this can lead to more offer turn downs if someone is not serious about making a career move. We even recently answered a question here on our blog about whether you can use a job offer in order to negotiate a raise at your current company (you can read our advice on that here), so job searching (even without serious intention) is top of mind for many data professionals.

3. WFH flexibility leads to increased opportunities and easier interviewing

There’s also the continued popularity of the work from home and hybrid models. Now, data scientists and data engineers don't have to move to San Francisco if they want to go work for a big tech company. Larger employers with WFH capabilities have been expanding their talent searches nationwide, and this means there are more people considering a wider array of options than ever before.

4. While West Coast & NYC still dominate, smaller markets are heating up too

Earlier this year, we gathered some data on which locations are seeing the biggest influx of data science, analytics, and data engineering job postings on Linkedin. The chart below includes mainly Data Scientist and Data Engineer job postings, but also many other related titles (Machine Learning Engineers, Cloud-related roles, Data Analysts, etc.).

While there are obvious limitations to a method like this, we were primarily interested to see if there was a directional trend to concentrations of postings based on location, not necessarily to precisely measure different cities against each other. What we found was that, while the usual suspects topped the chart (San Francisco Bay Area, New York, and Seattle), we can consider areas like Chicago, Boston, Austin, and Atlanta to be the “secondary tier” hotspots for data science & analytics jobs.We’re still gathering more data and monitoring trends, but it will be interesting to see how the prevalence of remote roles continues to impact the data science & analytics hiring market over the coming year. Early indicators suggest there will be salary increases for data scientists and analytics professionals in 2021, which will likely add fuel to the fire.

How are we seeing data science & analytics teams address retention and attrition struggles?

1. Evaluating your WFH options for the team

If your organization or team has been resistant to remote workers in the past, it is probably necessary to rethink that with the current market. From what we’ve seen so far, WFH, remote options, and schedule flexibility is front and center when it comes to retention, at least for the time being. This can also include offering flexibility to parents with small children while there is still such a piecemeal approach to COVID and quarantining for schools and childcare.In general, we’re seeing many companies shifting their policies depending on what other companies (especially their competition) are doing. When one company pushes back in-person policies, others will likely follow. We’ve also seen numerous situations where a data professional approaches us looking for a remote role because they were called back to the office, only to have their current employer offer to make their position remote or more flexible when the professional indicates their intention to leave – it shouldn’t get to that point! Make sure you know what your team prefers so that you can be proactive about addressing this employee retention concern where possible.However, so far, we’re seeing many leadership positions are still required to be on-site to more effectively onboard new people coming into those organizations, or that leaders must at least be willing to be on-site a significant amount of time in order to build relationships with other senior leaders, influence decision-making, navigate the business, etc.

2. Examine your team’s compensation and adjust where needed

Unsurprisingly, compensation is a strong concern for many when it comes to job searching. Our previous research found that salary is a key motivator for data scientists & analytics professionals when it comes to wanting to change jobs (you can see how motivations vary among a variety of factors here), and although we’re eager to update this research to tailor it to today’s climate, it’s likely that compensation is still very important to your team!One way to address this proactively that we’ve been hearing about are preemptive salary increases and retention bonuses as a way to decrease attrition, and with all the activity in the market right now (and hiring competition from remote teams all across the country), it may be something to think about for your organization as well.

3. Check-in with your team about career growth & other pain points

When it comes to increasing retention and stemming attrition, it's important for leaders and managers to understand their teams. Do their employees feel like they have a good next step in their career? Data professionals have a lot of options right now, so it’s important to listen to your team’s concerns and check-in with them. Sometimes even a minor change can alleviate a big concern.While of course a lot of job change decisions arise from seeking a pay increase, in our experience data scientists, analytics professionals, and data engineers are also looking for career growth and increased responsibility. They’re also looking for learning opportunities so that they can advance their careers and their personal knowledge. We hear about this a lot from data professionals: that if they don’t feel like they have opportunities to grow, don’t know what their career track is, or can’t see progression happening within their current organization, they’ll move on.Having these conversations with your team as a whole and individually is extremely important. It gives them a good foundation, helps them feel like they have a future at your organization and shows them that they can grow there, so that they don't feel forced to look externally to advance their career. Being proactive about these conversations will also help your organization be more proactive about addressing small pain points that can potentially lead to one of your employees leaving the company if left unchecked.

4. Data professionals want to work with the latest and greatest technology

Along the same lines as career growth and the other pain points mentioned previously, we know that data scientists, analytics professionals, and data engineers are very concerned with the importance of the technology stack and/or the applications that they’re working on for the business. This helps advance their skills and keeps them on the “cutting edge” of the industry evolution, which also makes your team better equipped to deal with new challenges.Also, we know that strong data science and analytics talent will want to work on interesting or important projects and applications that “move the needle” for the business. Clearly articulating the impact team members have on their company is an important best practice for engagement. This is a big piece when it comes to data professionals considering a career move and finding ways to address this can help decrease attrition.

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Need tips on hiring the right data engineering talent? Our new video includes hiring tips on: prioritizing skills and needs for your team, different types of data engineers, how they differ from data scientists, and other market trends that may impact hiring in 2021.