Analytics Recruiting: A Balancing Act

Featured article by Joe DeCosmo, Chief Analytics Officer, Enova International

While many believe the analytics talent crunch is real, others think there’s more hype behind this story than substance. The need for this particular talent set is growing, but in some instances companies have scrambled to hire data scientists and analysts at all levels – before they have a clear plan or bona fide need for these roles. This leads to experienced hires being asked to carry out administrative-like reporting tasks that should be geared towards entry-level positions. In addition, entry-level employees are often tasked with busywork that’s better completed by machines.

Overall, the hiring pool in analytics isn’t lacking candidates – but the right candidates can be difficult to pin down. Successfully balancing analytics hiring requires first determining the type of talent you need to meet business objectives, and then evaluating and hiring people with the skills and knowledge to move the needle for your organization.

Producers or Consumers?

Before hiring analytics talent, you should consider whether your company needs a producer of data or a consumer of data. Oftentimes, consulting firms or other service-based companies look for candidates who can read and understand data enough to make knowledgeable decisions on the data. However, other companies strictly need data scientists who can build predictive models and apply advanced data science. Ensuring you know the type of talent you’re looking for will enable you to right-size your team and understand what skills and knowledge are important for each role.

Quality vs. Quantity

My advice for those looking to hire analytics talent is to avoid over-hiring. The best implementations of an analytics department are starting with data scientists who can be hands-on from basic data preparation through modeling – don’t feel pressure to hire a bunch of senior-level analytics strategists to get the team running. You need the “machine” to run efficiently, but you also need the manpower to get the work done. If you’re just building your team, starting with small, well-defined projects that are sponsored by the business will enable your team to acquire some “quick wins” and prove value early on.

In order to find the right team members, be selective and interview wisely. During the interview process, present candidates with data set analysis and problem-solving questions as well as code review and comprehension sets. In addition to testing for skills, make sure to evaluate each candidate’s cultural fit and leadership capabilities. With the increased focus on data science and analytics, you need to make sure that candidates can do more than just talk about analytics, that they can actually deliver the work.

Creatively Expand Your Talent Search

The need to balance over-hiring and arming your company with enough skilled talent is just that – a balancing act. At times, it may force you to get a bit creative with the criteria you’re looking for in a candidate. Ten years ago, analytics professionals were built from those who studied math and statistics in college. Now, successful analytics employees come from many other quantitative areas of study, like computer science, industrial engineering, physics and economics. These quantitative skills are necessary and infused into many analytics programs.

At Enova, we currently have a history major building and analyzing models. He started in our business intelligence group and has been with us for five years. Good talent comes from all backgrounds and areas of study, so cast a wide net and don’t be afraid to take a chance if someone demonstrates solid quantitative skills.

The need to get more creative and broad in your talent search stems from the immense competition from the candidates primed for success. Oftentimes the top college graduates are being recruited by multiple companies at a time and have the upper hand in terms of what projects they want to work on and salary – driving the cost of hiring inexperienced talent upward.

This is also true for experienced hires. Candidates who have been successfully working in the industry for a few years or have even managed a small team can be harder to come by. However, if you start with smart people, investment in solid training and development can turn less experienced individuals into major contributors.

And What About Those Analytics Degree Programs?

There are multiple universities who have recognized the growing need for analytics talent and thus have developed analytics-focused programs, such as predictive or business analytic specialty areas. While these programs create people who are knowledgeable and give candidates a baseline of analytics skills, the candidates will truly learn how to data wrangle and build models when they are slotted into a high-functioning team. Given these programs are still in early stages, there is still some uncertainty as to whether they will lead to new types of analytic talent, or improve the types of skills that are already being taught. For example, rather than focusing on a new approach to developing a meaningful combination of business and technical skills, these programs can often feel more like a branding exercise, learning how to take existing content to try to capture the “buzz” of analytics and implement data in unexpected was.

In our experience, programs that emphasize real-world projects complete with messy data and ambiguous objectives rather than studying methodologies and working with curated, pristine datasets produce the strongest candidates that are ready to hit the ground running and get to work after graduation. While the programs are continuing to take shape, it is exciting to see colleges and university focus on prioritizing analytics-based education.

Trends in Analytics Recruiting

Analytics departments are growing – and fast. At Enova, our analytics department has doubled in size since the beginning of 2014. If your team is growing quickly, you can capitalize on the available talent by campus recruiting and other creative hiring tactics. Some creative tactics we’ve used include coffee booths on campus, information sessions, analytics competitions with prizes, networking receptions and hackathons.

Be social and don’t forget the power of your network. Our recruiting team will often invite analytics team members to participate in “Spotlight Parties,” where analysts will have lunch together while reaching out to members of their network who they think might be interested in a particular job opportunity.

There is no doubt that companies will need to continue to build their analytics team, but having an understanding of the type of talent that you need, in addition to understanding the talent landscape will be critical in making sure you are able to staff the best team to meet your business objectives. By first establishing your needs and hiring criteria, and then thinking outside of the box when it comes to finding and training talent, you can be well on your way to building an effective and impactful analytics team that drives results for your company. Keeping an open mind when it comes to the types of education new recruit have, and looking towards educational programs that are teaching the types of skills that will move your analytics program forward opens up the opportunity for you to attract the best and brightest talent.

About Joe DeCosmo

Joe joined Enova as Chief Analytics Officer in 2014. Prior to working at Enova, he served as Director and Practice Leader of Advanced Analytics for Chicago-based West Monroe Partners. He also held a number of executive positions at HAVI Global Solutions and the Allant Group. Joe received a BA in Economics from Lewis University and an MA in Economics from the University of Illinois at Chicago. He currently serves on the boards of the Chicago Chapter of the American Statistical Association and the UIC College of Business Administration. Joe lives in the Chicago area with his wife and daughters. In his spare time, Joe enjoys competing in USTA tennis matches across the Midwest — and he’s rumored to have a mean backhand.