With high demand and lagging supply, hiring data scientists in today’s market is no easy task. To help you find and hire them, we distilled key findings from our research, including: where to find them, what they know, what they value, and more.
For better or worse, in a tight labor market, understanding your candidate personas is a key prerequisite to attracting and winning technical talent. Learning your target candidate profile—from the skills they have, to the benefits they want, and more—can be the difference between winning and losing top talent.
So if you’re hiring data scientists, what should you know before you start your search? We gathered insights from our 2019 Developer Skills Report—based on feedback from over 70,000 developers and technical professionals—to better understand. In this guide, we’ll unpack data from our research to help you better find, attract, and evaluate data scientist candidates.
Here’s what we’ll cover:
As a whole, data scientists make up a relatively small portion of the tech talent pool (2%)—but more than 1 in 4 are currently looking for work.
Despite their increasingly high demand, data scientists are somewhat rare, representing roughly 2% of the tech talent population. Within that subset, roughly 31% of data scientists consider themselves to be a “junior” level employee. On the other hand, 29% say they’re currently acting in a “senior” role.
They’re also more likely than average to have sought a job within the last year: 44% of data scientists have looked for work in the past year, as opposed to 38% of people in other tech roles. That spike in job seekers could be tied to the growth in the field. While 2014-2017 marked a a massive boom in companies hiring data scientists, it’s since begun to slow.
Data scientists are less likely than other roles to be searching for work right now, but were more likely than other tech roles to have sought work in the last year.
Their biggest differentiator, however, is their educational background. Data scientists are twice as likely to have a Master’s degree or Doctoral degree compared to the average technical professional: 55% hold both a Master’s and a Doctoral degree, compared to 24% of those in other technical roles. While the growing popularity of data science nanodegrees may shift that educational profile over time, the data science talent pool is still largely dominated by those with advanced degrees.
Data scientists work primarily at companies with <1,000 employees, and are geographically spread across the world: from China, to Israel, to the UK, and beyond.
To find passive data scientist talent, smaller companies are your best bet: roughly 59% of data scientists currently work at a company with less than 1,000 employees.
The United States has the largest population of data scientists (30.1%), followed by India (23.7%) and Brazil (5.4%).
Like other technical talent, they’re easy to find on public projects websites—78% say they have an account, and 63% say they’ve submitted public projects within the last year. But geographically, they’re well distributed across the world. They’re most concentrated in the United States, where roughly 30% of data scientists reside. But they’re also well distributed across the Middle East, Europe, and the Asia-Pacific Region, amongst others.
Data scientists are most likely to know Python, SQL, and Java, and spend the majority of their days coding new features.
Day-to-day, they spend most of their time on coding new features (58%), brainstorming (47%), and fixing bugs (41%). But compared to the average technical professional, they spend less time on maintenance tasks (e.g. fixing bugs and resolving tech debt), and more time on new initiatives (through tasks like brainstorming and new product innovation).
Most data scientists want to work remotely on a regular basis, and consider growth & learning their top priority when job seeking.
Today, over half of data scientists work remotely only on occasion. But even though the majority spend their time in the office, most crave flexibility: 73% want to work remotely at least 1 day per week. They’re most excited about learning languages like Scala, Go, and Julia.
All technical professionals both value growth & learning opportunities, but data scientists value having interesting problems to solve more than other roles.
In a job hunt, their priorities differ from other tech roles. Notably, they care less about competitive compensation, and less about work-life balance than people in other tech roles. But on the flip side, they care more about having interesting problems to solve at work. So while competitive compensation and work-life balance are important selling points, it’s worth emphasizing the business problems they’d solve, too.
Most data scientists expect to be interviewed by 5 or less interviewers, and prefer take home projects for technical evaluations.
When it comes to hiring data scientists, a small interview panel is ideal. In fact, roughly 81% of data scientists expect an interview panel of 5 people or less. And for technical evaluations, they prefer more flexible forms of evaluation, like take home projects and online coding challenges.
Like other technical roles, they’re turned off most by lack of clarity, slow follow up, and culture fit mismatch. That said, they’re more turned off by lack of (or slow) followup than the norm: 60% of data scientists find this frustrating, compared to 54% of people in other technical roles.
With high demand and a lagging supply, finding data scientists isn’t easy. If you’re looking to hire for data science roles, keep these trends in mind:
Data scientists comprise only 2% of the tech talent population—so they may not be easy to find. To find passive candidates, your best bet is to look at companies with under 1,000 employees. They house the lion’s share of working data scientists (59%).
For better or worse, data scientists are geographically concentrated in the United States and India. But with pockets of talent well distributed across key business regions (e.g. Asia-Pacific, the Middle East, and Europe), it’s not impossible to find candidates within your region.
Most data scientists (53%) work in an office, with only occasional opportunities for remote work. But that doesn’t mean they’re disinterested in remote work. In fact, 73% want to work remotely at least 1 day per week.
This spells opportunity for enterprising orgs: few data scientists work remotely week-to-week, but the majority would like to. Consult with your hiring manager: are they comfortable with a data scientist that works remotely on a regular basis? If so, it could be help you compete for talent that might otherwise overlook your org.
When it comes to job seeking, data scientists share the same priorities as other technical professionals: growth opportunities, work-life balance, competitive compensation, interesting problems, and flexibility. But data scientists don’t prioritize them in the same order.
Instead, data scientists care less about competitive compensation, less about work-life balance, and more about having interesting problems to solve. In fact, having interesting problems to solve is their #2 priority in a job search (outranked only by growth opportunities). Tasked with using data to improve the business, data scientists are driven by the complexity of your business challenges. Talk to your current data science team to understand which problems excite them the most. Highlighting those problems can help you attract data science candidates, even if your org is a “non-tech” brand.
Data science is a candidate’s market. Especially for emerging tech talent brands, offering flexible work environments, and highlighting the nuances of your business problems will help sell them on your org.
Want to learn more about what’s motivating today’s tech talent? Check out our 2019 Developer Skills Report: