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This post is an excerpt from our upcoming 2019 Data Science & Predictive Analytics Salary Report. Register for our webinar on June 20th to learn more about the latest salaries and hiring trends.

With the distinction between data scientists and professionals in analytics roles growing fuzzier by the day, this year, for the first time, we’ve combined our separate Data Science and Predictive Analytics salary reports into one.

We’ve always regarded data scientists as a specialized subset of predictive analytics professionals, and our hope in presenting these two sets of data side-by-side (instead of in separate studies) is to show some of the interesting comparisons between the two groups that we’ve noticed over the past few years.

Burtch Works has typically segmented data scientists and predictive analytics professionals because of skillset differences that led to differing salary bands. As we’ve defined them, data scientists typically work primarily with unstructured or streaming data and therefore command higher salaries than others in predictive analytics that mostly focus on structured data. Although the two areas are becoming more blended as of late, there are a number of reasons why we’ve continued to analyze them separately that we’ll be highlighting throughout this report.

For our hiring market trend analysis in this section, we wanted to highlight the trends that are affecting the quantitative hiring market from our unique vantage point as recruiters working with such a wide variety of quantitative teams.

 

8 Quantitative Hiring Market Trends from This Year’s Report

1. Predictive analytics tools are becoming more robust

Open source tools and vendor-provided solutions have made implementing what used to be a complex and rigorous math and computer science problem more accessible to a wider range of individuals. While data science tools are far from a “point and click” stage, they’re maturing quickly.

 

2. Educational backgrounds are continuing to shift

Alongside the maturation of data science tools and an increasing number of education options (such as bootcamps, online courses, and Master’s programs targeting mid-career changers), we’ve noticed some shifting in educational backgrounds. Among predictive analytics professionals with 0-3 and 4-8 years of work experience, there was a noticeable uptick in the proportion of these segments whose highest degree was a Bachelor’s.

 

3. Quantitative professionals have more options than ever

The accelerating proliferation of data science and analytics initiatives across the country and across a multitude of industries has led to quantitative professionals having more options than ever. Not only geographically and industry-wise, but options have also become more technically diverse, as more industries continue to mature and explore new potential use cases for their data teams.

Among quantitative professionals there is increased interest in so-called “mission driven roles” that have some sort of societal or environmental impact (such as using data to improve health outcomes or energy efficiency, or to support non-profit work).

 

4. Emergence of the “data science citizen” role

With many quantitative teams increasing their size and scope, many businesses are grappling with the challenge of how best to liaise between the technical team and other business unit stakeholders. Companies sometimes staff up with a so-called “data science citizen”, who is typically a professional with a data science or analytics foundation who has sufficient technical depth along with the business acumen necessary to translate between the quantitative team and professionals in other areas like the Marketing or Finance team, or even those in the C-Suite.

These professionals, who may have a range of job titles, are the stewards of data science initiatives. They use their expertise to be representatives for the quantitative team and free up data scientists to do what they do best: advanced technical analysis. Both types of roles are important to a successful quantitative team, and we’re seeing more interest in the data science citizen role as team sizes continue to expand.

5. Companies turn to satellite offices to strengthen their bid for talent

To those looking to hire data science and analytics talent, it’s no secret that the location of your data science team can help or hinder your hiring results. We’ve seen many companies opening satellite offices in multiple urban areas to increase their role’s marketability.

Major companies in San Francisco especially are continuing to expand their talent outreach with satellite offices as candidates grow wary of the high cost of living in the area.

 

6. Technical screens/testing are becoming a staple in the quantitative hiring process

With more variety in educational backgrounds than ever before, technical screens and testing are swiftly becoming “must haves” in the hiring toolkit. Most quantitative professionals are still coming from more traditional educational backgrounds such as a Master’s or PhD program. However, many have also supplemented their skillset with online courses or coding bootcamps in order to stay up-to-date.

Seeking to better evaluate talent that come with a wide variety of educational backgrounds, many hiring managers are relying on testing to measure technique and tool fluency. Additionally, we continue to see case studies used to assess problem-solving capabilities and business acumen. With all of these methods, it’s important to remember to keep the hiring process moving and keep time commitments reasonable in order to compete in today’s hot market.

 

7. As demand increases for specialized skillsets, the search becomes more challenging

Companies looking to drill down on specific use cases are focusing more on data scientists that specialize in particular areas, such as NLP (natural language processing), Computer Vision (image processing), or even specific domain experience like AdTech or IoT (internet of things). Of course, as demand becomes more specialized, this also narrows the potential talent pool to draw from.

The market used to be more focused on do-it-all “unicorns” which are also difficult to find (on the opposite end of the spectrum), but now that analytics at many firms is maturing, organizations are pivoting to seek out more specific skillsets that can elevate their capabilities to the next level.

 

8. With more companies expanding data science capabilities, a push for visionary leaders

As so many organizations recognize the value add of data-driven decision making, there is a strong need for leaders who not only have compelling leadership skills and a strong understanding of analytics, but who are also well-versed in new or emerging techniques and who can explore new opportunities for the business. Having a strong analytics leader at the helm who can recognize potential use cases and push what’s possible is crucial to success in today’s competitive business landscape.

 

Want to learn more about quantitative salaries, demographic data, and trends affecting both professionals and employers? Register for our webinar!

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