How Are You Combating the War on Data Talent?

(US & Canada) After a slowdown in 2020, hiring and job search activity in the data science and data engineering fields rocketed upwards in 2021. Both of our 2021 data talent hiring surveys showed many teams are hiring, and we’ve been having countless conversations with hiring managers and executive leaders centered around retention challenges and an increasingly tough battle for data science and data engineering talent. 

According to our survey respondents, who represented over 125 companies across the US, we found that 81% of data science and analytics teams were planning to hire during Q3 or Q4 of 2021. This included plans to hire data scientists and data engineers, who continue to become an increasingly key part of data teams. 

What Factors are Spurring the Data Science & Data Engineering Hiring Market?

1. With data-driven decision making becoming essential for companies to stay competitive, there is a strong hiring need for data talent as these digital transformation efforts continue to grow.
2. The proliferation of remote roles increases access to more talent for companies and access to more opportunities for data professionals.
3. More data professionals evaluating potential career moves in 2021. While many put job searches on hold in 2020, this year we’ve seen an incredible number of data & technology professionals either actively or passively evaluating their job options or reactivating their searches.

With competition for data science and data engineering talent so strong, and with remote roles offering more access to opportunities for data professionals, an effective talent management strategy must include both hiring and talent retention efforts. Losing out on data talent is costly. Whether it’s due to competing offers during the hiring process or losing institutional knowledge and experience due to attrition – hiring and retention challenges can significantly derail business priorities.

What Industries are Pushing the Hiring Acceleration?

Financial Services and Marketing/Advertising firms have long employed predictive analytics methods, but while these industries still have a strong need for talent, they may not necessarily be increasing capacity at the same rate as others. With new startups continuing to push the boundaries of analytical applications, this has only exacerbated competition for available talent. 

Data engineering, data science, and analytics have all been experiencing a hiring push towards Healthcare as data teams in this industry continue to expand. Our market reports have examined industry segments for years, and over the past five years we’ve seen the percentage of analytics professionals employed in Healthcare nearly triple. The Healthcare industry also employs 18% of the professionals in our data engineering sample, making it the third-largest industry segment for data engineers. This has become especially significant due to the challenges of the COVID-19 pandemic, but, at least from our data on other technology fields, this trend was already picking up steam long before that.

Most recently, we’ve also seen some expansion in Retail, with some salaries in this industry being uncharacteristically high in 2021 compared to past years. Many retailers, especially those with a strong ecommerce presence (or those who pivoted their 2020 business plan accordingly), have been expanding their teams and offering higher compensation than we’ve typically seen in retail. While retail salaries might not be on par with the typically-high tech industry salaries, digital transformation and ecommerce are leading the way for the retail industry as a whole to be more competitive in the hiring market.

Over the past few years, a key takeaway from our reporting was that there has been a proliferation of machine learning and deep learning in more traditional industries, as well as an increase in innovative startups in a variety of industries. While many data scientists may have felt in previous years that the most advanced data science applications were only to be found in West Coast tech firms, this is not necessarily the case anymore, as there are advanced data science applications that are being applied in every industry. The Tech industry is largely considered to be highly-paid and a well-represented employer of many data engineers and data scientists, but with increased hiring and applications in nearly every industry, it is increasing talent competition for all employers.

What Factors Motivate Attrition?

So how can executive leaders prioritize retaining their data science and data engineering talent? Recently, we surveyed our vast network of data professionals to take a pulse on what motivates them to stay at their job. Respondents were asked to pick their top three factors, and we received over 350 responses to our survey, which included a mix of data scientists, analytics professionals, and data engineers. 

As one might expect, our overall results showed that base salary increase was the most popular choice with 43%. Three different options tied for second most chosen at 41%, which included good management or leadership, flexibility (which includes WFH, flexible hours, etc.), and interesting or challenging work projects.

We also segmented the sample to see whether there were noticeable differences between different groups. We found that while early career professionals are more likely to prioritize growth opportunities, senior professionals tend to find that interesting/challenging work is more important to them. Managers favored good management/leadership, which might be interpreted as the importance of having strong leadership above them as well as executive buy-in. You can read more of our analysis here.

How Should Data Teams Manage Compensation Expectations?

With salary expectations playing such a crucial role in hiring and retention strategy, Burtch Works has been producing market-leading salary reports since 2013. We’ve found that many of our clients and candidates are keenly interested in how different factors impact salaries, either by their job level, industry, education, or other demographic factors. Our 2021 salary reports for data engineering professionals and data scientists can both be downloaded for free here.

Although our attrition survey analysis found that, for most professionals, bonuses may not be as enticing as base salary increases, one of the interesting insights we found from our data analysis for our data engineering report was that bonus eligibility in data engineering is relatively high. Our data showed that 80-88% of individual contributors are eligible for a bonus (depending on their experience level), which increases to 95-96% of managers. A key takeaway for employers is that although variable compensation may not be a substitute for a base salary increase, the vast majority of data professionals we speak to expect some form of bonus to be part of their compensation package.

How Should Data Teams Fight the War on Data Talent?

As we’ve advised before, in order to boost retention, it’s critical for leaders and hiring managers to have regular conversations with their data teams in order to understand their priorities at work. A “one size fits all” approach may not work ideally for all staff, and, especially now, the conversations around remote working have been constantly shifting. We’ve seen many employers expand remote working options to access talent in more markets, and we’ve even seen some employers adjusting their WFH policies with the specific intent of luring talent away from their competitors (or to stem attrition).

We also highly recommend reevaluating your hiring process with the goal of streamlining the process to secure talent faster. Most employers have been using the convenience of virtual interviews to move through the process more quickly, as well as prioritizing access to the hiring team in order to greenlight potential hires where applicable. Additionally, while in previous years we had seen a number of technical assessments or assignments during the hiring process, it’s important to be cognizant of the time commitment for a candidate and how that may impact your ability to hire (especially when candidates are fielding multiple offers).

We’d also be remiss to not mention the importance of competitive compensation packages, including salaries, bonuses, and, in some cases, retention bonuses. We’ve also seen an increasing emphasis on equity grants as a way to entice candidates, particularly for startups. As we’ve seen in our attrition research, candidate work priorities may vary, so benefits packages that can be flexible to accommodate different preferences (such as offering more vacation time vs. offering training) may prove more enticing.

While this has been a strong summary, our full industry reports for data science and data engineering offer a plethora of data and insights that are crucial to managing talent strategy, including WFH, industry trends, salaries examined by various factors, attrition insights, and more. We’re able to provide this data to the community because of our unique position between both employers and data professionals, which gives us an exceptional vantage point on market trends. To access even more information to aid your battle in the talent war, you can download the full reports here.

The Founder & Managing Director of Burtch Works, Linda Burtch is an industry leader in data science & analytics recruiting. Linda began her career on the corporate side at Pepsi and Whirlpool, before transitioning into recruiting and becoming a subject matter expert on the analytics and data science hiring market. Linda is a frequent speaker on career and hiring topics, and has been an active member of the Chicago Chapter of the American Statistical Association and INFORMS for years, including holding several board positions. She frequently shares her market insights at luncheons, conferences, corporate meetings, and webinars, and has maintained a popular blog on the quantitative hiring market for over 15 years.