9 Tips For Hiring Data Science Talent
Data scientists are the new corporate rock stars. Companies in all industries know they need a strong bench of data science talent to have any hope of becoming the next Uber or Netflix. With more demand than supply, here's what you need to know about recruiting the best and brightest.
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Data science and data analytics skills are in high demand, and the race is on to attract and hire top-notch professionals who possess these skill sets. That's no surprise. Establishing a data science practice inside an organization can provide a competitive edge against rivals in an age in which every company wants to be the next Uber or Netflix.
Companies are using data to gain insights into customer needs, provide customers with new products and services, and give themselves an edge against competitors. Having the right professionals to wrangle the data is crucial as organizations collect more than ever before.
Complicating matters further, the information being collected is made up of myriad data types, including some that are new to the organization.
As companies look to launch a data science and/or analytics practice internally, they're competing against each other to woo data science pros, the new rock stars of the business world.
How do you attract them? It isn't really enough to slap a job ad on LinkedIn or Monster and hope for the best. These professionals are rare and you can't simply broadcast your message and hope they show up on your doorstep. While you're waiting, your competitor may be taking your top prospect out to lunch and developing a relationship.
[Data science is an attractive career for women in technology. Read 12 Inspiring Women in Data Science, Big Data.]
Linda Burtch is managing director of Burtchworks, an executive recruiting firm that specializes in finding quantitative professionals for its 120 clients. In an interview with InformationWeek, Burtch said today's job market for data professionals is the busiest she's seen during her 30 years in the business.
"This is absolutely the highest demand for this kind of professional that I've seen in my career," Burtch told InformationWeek. Data scientists -- quantitative professionals who can work with real-time, streaming, messy data types -- are in particularly high demand. There aren't very many of them.
The industry refers to these rare specialists as "unicorns." According to Bob Rogers, Intel's Chief Data Scientist, these professionals typically possess three different skill sets:
High-end mathematical and statistical knowledge
High-level computing skills with programming languages such as Python and Scala
Deep expertise in a very specific business or domain, such as healthcare or finance
Yet in our ongoing conversations with IT and business professionals we've heard repeatedly how tough -- if not impossible -- it is to find a single individual who possesses all three competencies.
With that in mind, InformationWeek has culled recruitment advice from Burtch, along with insights from data scientists we've interviewed in the past year. Here are nine tips to help you find the best and brightest data science talent.
Once you've reviewed our guidance, please share your own advice, insights, and experiences in the comments section below.
There are a number of high quality data scientist Masters and PhD programs at universities today. According to Burtch, some organizations have aligned themselves with these programs, recruiting students when they graduate. Two strong university programs are the Berkeley School of Information at UC Berkeley (which also offers an online Masters program), and Northwestern's Master of Science in Analytics, which offers a separate online Master's degree in Predictive Analytics.
Candidates with advanced degrees in one scientific specialty may pursue specialized data science knowledge. Others may look to broaden their career options by exploring data science as a possible new direction. These professionals may attend data science boot camps.
Such programs are typically 12 weeks in duration and are designed to give highly educated and qualified candidates the knowledge they need to start a career in data science. Such programs often also offer candidates placement upon completion of the coursework. It's worth contacting these training organizations when looking for your next data science job candidates.
Job candidates who hit on all the core competencies are called unicorns for a reason: They are so rare they may be impossible to find.
Instead of hunting mythical creatures, experts recommend spending your time pursuing candidates who have one or two of the core skills you need. If your candidate knows Python, chances are he or she can also learn R or another language. Keep an open mind and consider your candidate's future development, too.
If you can't find a unicorn, try growing your own. How about offering training to new hires to get them up to speed? You might try hiring several candidates with strengths in different areas and creating a team where they work together. Some organizations "embed" data scientists within their business teams. This allows the data scientist to lend their own expertise while tapping into the domain knowledge of colleagues who work in the business segment day-to-day.
It's important to treat your data science candidates like the rock stars they are. Even if you plan to reject their application, Burtch said, "it's important that you leave them in a good way. They all talk to each other." Here are two ways to show job candidates you value them: Don't leave them waiting a long time in the lobby before an interview, and demonstrate how your corporate culture is respectful of their talent and their time.
Once you engage with a candidate, don't leave the person hanging while your organization gets its act together, Burtch said. You may end up losing a good candidate if you make one person wait until you have a slate of five potential candidates to interview all at once. Make sure you are ready to move fast before you engage.
As an executive recruiter, this approach makes sense for Burtch. Her firm stays in touch with candidates throughout their entire careers, beginning from the time they complete a degree. "Many of the candidates I've worked with now have become clients," she said. These folks are now turning to her firm for help hiring the next generation.
LinkedIn, which has built an impressive data science practice internally, seems to take a similar approach to recruitment. In his book The Alliance: Managing Talent In The Networked Age, LinkedIn's executive chairman and cofounder Reid Hoffman described the company's relationship with employees as "alliances," or "tours of duty," in which employees sign on for two to four years, rather than promising undying loyalty. LinkedIn data science alumni have gone on to found big data startups or serve as in top data scientist roles at other companies. Hoffman said in his book he considers these alumni to be allies, and continues to work with and stay in touch with former employees.
Don't know where to start to build your data science talent pipeline? Try Kaggle, a destination site for data scientists. It offers them a repository of publicly available data sets, a place to showcase their work, a community area, and a job board. It famously lets companies and organizations host data science competitions in which participants are given a problem to solve. Winners receive prize money, plus coveted visibility in the data science community.
Looking for a place where data scientists gather locally? Consider attending a data science meetup. You can find your local meetup by searching the Meetup website. These networking groups can connect you with up-and-comers who may have been overlooked by your competitors.
Looking for a place where data scientists gather locally? Consider attending a data science meetup. You can find your local meetup by searching the Meetup website. These networking groups can connect you with up-and-comers who may have been overlooked by your competitors.
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