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Following a slowdown in 2020, hiring and job searches in the data science and analytics fields increased once again in 2021. Our analysis reveals that 33% of data scientists changed jobs in 2022, compared to only 17% in 2017. This year’s data science & AI professionals salary report is of great interest since its timing aligns well with the economic recovery that began in Q2 of 2021, which then led many data science and analytics professionals to ramp up their searches. Unfortunately for those looking to hire, this has led to fewer professionals on the market in 2022, and a lower inventory of available candidates. As such, that has presented difficulties in hiring and, until very recently, our research from earlier in the year indicated the vast majority of data teams were still looking to hire this year.

What is Causing the Hiring Challenges in Data Science & Analytics?

  1. Data-driven decision-making is essential for companies to stay competitive, and during the past couple of years, the economic disruption and digital transformation in the financial services industry have driven institutions to increasingly invest in data science and AI hires.
  2. The spread of remote roles, driven significantly by the pandemic, allows companies to access more talent than ever before for companies, while also offering candidates more access to opportunities.
  3. Furthermore, the total number of international students at universities in the US declined by 15%, impacting the pool of available early-career candidates in data science & analytics.

As a result of this heightened competition for data science and analytics talent, it is important that companies focus on both hiring and talent retention. From our recent conversations with companies and candidates, we learned there have been massive changes during the past few years. Companies are starting to understand and appreciate the need to streamline their interview process to facilitate the hiring process. Notably, technical assessments and coding exams are becoming less frequent during interviews, and companies are instead evaluating whether they can eliminate a skills gap through training and mentorship.

Impact of the Great Resignation on Data Teams

Besides there being less talent actively available on the market in 2022, we’ve seen a rise in counter offers as teams compete to retain staff, and frustration on the part of both hiring managers and the coworkers who are left behind to pick up the slack. As such, we have witnessed companies preemptively giving retention bonuses as a sign of their gratitude for work done on projects, not to mention raises prior to the traditional schedule.

The attitudes towards remote work and hybrid schedules are continuing to shift and can sometimes show a disparity between employer and employee expectations. While we know that the vast majority of data scientists and analytics professionals favor a hybrid schedule, companies may not always agree to accommodate this new trend to the degree that their employees seek. As we find in our salary study this year, only two percent of employees prefer being fully office-based, and about 36% of clients are open to hiring fully remote.

With an abundance of opportunities for candidates, they are increasingly looking for a streamlined interview process, take into consideration WFH flexibility, and explore sites such as Glassdoor to evaluate a company’s culture and work-life balance prior to signing an offer.

 Factors Driving Attrition for Data Science & Analytics Talent

Apart from the hiring challenges caused by the Great Resignation, considering retention strategies and the types of benefits that appeal to your data science and analytics talent is important. Last year, we surveyed our vast network of data professionals to ask 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 and analytics professionals.

Unsurprisingly, we found that a base salary increase was the most popular choice at 43%. However, there were several options that tied for second place at 41%, which included good management or leadership, schedule flexibility (WFH, flexible hours, etc.), and interesting/challenging work or projects. We also segmented the sample to see whether there were noticeable differences between different groups, and you can read more of that analysis here.

As we pointed out earlier, in 2022 there has been an increased focus for some professionals on fully remote roles, and, in almost all cases, data professionals want at least some sort of hybrid office/home schedule. Consequently, we found that 34% of respondents, the highest number, prefer to be in the office two days a week, with 24% preferring 3 days in the office. Exceedingly small numbers of respondents look to be in the office 4 or 5 days a week. The rise of WFH (work from home) during the COVID-19 pandemic, the subsequent Great Resignation as many professionals began to reevaluate their current positions, and other factors have all played a part in altering how professionals view work. This has resulted in a number of knock-on effects that continue to evolve and impact teams looking to hire or retain data talent.

The Impact on Salaries

Because the job market became highly active starting in April of 2021, we analyzed a sample of data scientists and data engineers who changed jobs in Q2 through Q4 of 2021, to better understand how salaries increased. We found that 51% of the sample received a base salary increase of 20% or more. As we detail in this year’s Data Science Salary Study, the median base salary for data science professionals increased the most, by 15%, for the MG-1 compared to 2021 levels. That was followed by a 13% increase in median base salary for data science professionals at the IC-1 level. The average salary increase for data scientists and data engineers who changed jobs in 2021 was 20%, which overwhelmingly outpaces the market average of a 6.6% increase for job switchers.

With salary expectations playing such a crucial role in hiring and retention strategy, Burtch Works has been producing market-leading salary reports since 2013. In between our 2021 and 2022 reports, we analyzed a segment of data science and analytics salaries to see if the median and mean base salaries were beginning to show increases from the activity in the hiring market. We found that, from April 2021 to November 2021, data science salaries had already increased by over 8%, while analytics salaries had already increased by over 12%. You can view the rest of our analysis on salary increases here.

Salaries are of course impacted by many varied factors, including job level, industry, education, or other demographic factors. Our salary reports for analytics professionals and data scientists examine compensation by a multitude of different factors to give a more accurate picture of how this varies, and they can both be downloaded for free here. Education, for example, has historically had an impact on salary, and we find that 93% of data scientists and AI professionals surveyed had an advanced degree. Business and mathematics are the two most popular areas of study for data scientists, while engineering is an area of focus that is gaining prevalence both among data scientists and AI professionals. From a geographical perspective, data scientists have the highest median base salaries on the West Coast, with the Northeast coming in second place.

Keys to Beating the Hiring Crunch

How can teams that are looking to hire data science or data engineering talent overcome some of these hiring and retention challenges presented by the Great Resignation? Firstly, it’s important for leaders and hiring managers to communicate with their data teams to understand their priorities. Positive relationships can go a long way towards retaining your employees. Building strong relationships among the members of your data team can lead to stronger cohesion and impact retention. As noted earlier, 41% of data science professionals picked good management as a top factor determining their happiness at work, so leaders and hiring managers should be attuned to that.

We also recommend establishing partnerships with schools to build a pipeline of early career talent, and in some cases, it may be possible to build up or train internal talent to become citizen data scientists for certain roles. Emphasizing the broader impact and business value that your data talent brings to the organization and fostering a positive culture toward data science can also be key factors in both retention and hiring. It is also important to maintain relationships with company alumni as we have seen several candidates return to their previous companies for a variety of reasons.

Evaluate 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, and carefully evaluate the need for technical assessments as that may deter talent during the hiring process.

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 to entice candidates, particularly for startups. For more of our tips for employers on reducing impacts from the Great Resignation, click here, and to access our data-on-data science and analytics salaries, as well as developing market trends, you can download the full reports here.

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