As the industry obsesses about how many million data science job openings will go unfilled over the coming years, we wanted to take a closer look at the details of how those jobs are defined and challenges in recruiting.
In order to get an understanding of the variation when it comes to data scientists profiles, it is sufficient to take a quick look at LinkedIn. We see a variety of educational backgrounds, job experiences, and skills listed by professionals for the same job title, even at the same company. For example, here are three data scientists at Facebook:
The variety in ways how companies define their data science jobs is also quite stunning. The following three job descriptions are from three companies in the same industry:
"DS & Analytics Roles remain open for 45 days, five days longer than average."
With this in mind, the complexity of the hiring process causes the job-postings to stay open for longer periods, thus increasing the hiring costs as indicated in IBM’s The Data Science/Analytics Landscape report. The report says, on average, Data Science & Analytics roles remain open for 45 days, five days longer than average.
"There are many Data Scientists strong in engineering, but the math is rare."
Here is a quote from an industry executive describing the problems they face during the recruitment phase for open positions:
“I just hired a data scientist and we started with about 60 applicants. The role was fairly well described and as a result I immediately eliminated 40 without a screening call… So I got down to about 20 for screening calls… down to 5 interviews onsite. At the end none of them were acceptable except for one. So from 60 to 1, this is a huge effort. After the screening, the ones that actually made it to interviews almost all of them failed on the math questions. There are many of them strong in engineering but the math is rare.”
Let’s also keep in mind that often these lengthy interview steps are carried out by highly skilled employees and already scarce resources within the organization. Rick Brownlow, who is the Co-founder and CEO at Geektastic, explains the 6-step hiring process for an engineer role in Silicon Valley and all direct & indirect expenses for each step:
1. Create a proper job description = $300
2. CV screens = $500 via recruiter or $1,250 direct hiring
3. Phone screens = $750 via recruiter or $2,250 direct hiring
4. Code Challenges = $ 4,160 via recruiter or $ 9,000 direct hiring
5. Phone - tech follow ups = $1,575 via a recruiter and $3,825 direct hiring
6. Final Face to Face interviews = $10,800
When all these costs add up, that’s $30,000 via a recruiter (including revenue share), or $27,500 direct. The last 3 steps of the process are especially more expensive since some engineers from the team take part in the process as they help assess candidates on technical skills with code challenges etc.
"Candidates are getting bombarded with job offers. That leads to a lot of frustration and cultural impact on the organization."
There is also the irreversible loss of time and money when the company hires an unqualified candidate and the challenge to keep them:
“You want to curb attrition and that ends up affecting your decisions on recognizing and promoting people between levels which might be inconsistent with actual skill sets and how they're progressing in their roles. But I don't see a solution for either because the market is so hot or they're getting bombarded with job offers. And that leads to a lot of frustration and cultural impact on the organization”
Do you have an experience to share or comments on the topic? Email us at info@iadss.org. You can also contribute to the research via a 10-minutes survey, aiming to get insight into analytics organizations, team structures, and skill-set expectations for Data Science and Analytics roles.
We believe defining professional standards in job titles, associated skills and tools for assessment, it is possible to reduce the confusion and waste of resources in the industry. Our research leverages multiple data sources and our findings will be synthesized in a comprehensive report:
Survey: Collect detailed information on data science & analytics roles, expected skills & knowledge to fulfill them.
1-1 Interviews: Gain insight into organizational and recruitment practices by talking with analytics executives and hiring managers.
Literature Review and 3rd Party Data: Cover academic resources and previous research around the topics as well as conduct market analysis on job platforms.
Our effort to define standards for analytics professions continues and insight from the research are shared on our blog. You may join the survey before it closes soon or ask to be notified of the research results:
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