The Data Scientist is one of the most sought after positions in tech – and much of the business world. But the demand is very quickly outstripping the supply.
McKinsey estimates that by 2018, the U.S. economy will have a shortage of 140,000 to 190,000 people with analytical expertise. And if you’re a startup or business that’s looking to expand your data science operations, that might sound a little scary.
How can you attract top talent in such a competitive market?
We’re not saying it’s going to be easy, but with these tips from Data Scientist at Insightly, Sara Vera, it’s certainly going to get a lot easier!
In her keynote at Extract SF 2014, Sara gave us her top three tips for hiring great Data Scientists. And now, she’s sharing them with you! Watch the quick 10 minute video of her talk and then read on for a more in depth look at her insights.
Want more great talks? Check out Extract!
How to hire data scientists when demand is higher than supply
Because the field of Data Science is so new, a lot of Data Scientists – like myself – fall into it in a very nonlinear way.
I was originally in a PhD program at the University of Washington, but after I finished my masters degree I decided I didn’t want to stay in academia. I wanted something a little more fast paced. When I started browsing job websites, I realized that my quantitative skill set was pretty valuable in the private sector. I decided to move to San Francisco because I liked how cutting edge the tech industry was and it felt like a great place to deepen my skill set.
When I got to San Francisco, the demand for Data Scientists was growing fast – way faster than the supply. As a job seeker it’s great. But once I got a job at Insightly and needed to hire more Data Scientists, it became super challenging. I’m going to share with you what worked at Insightly and give you some tips that should help you hire some great people.
Define your needs
Because data science is such a broad field it really helps to first define what you need in your company.
Are you looking for somebody who is going to be producing analytics for machines or for humans?
The reason you want to do this is because those are pretty different skill sets, even though they are both done by Data Scientist. For example, if you’re looking to build ad-targeting or recommendation engines or do algorithmic training then you are going to want to look for somebody with a really strong mathematics and computer science background.
On the other hand, if you are looking for somebody who is going to be reporting to project managers and diving into how the product is doing or how your user growth is going; then you need to find somebody who’s really good at storytelling. That type of job requires someone who is able to connect what could be pretty fuzzy data points into an overarching narrative of what’s going on in your company. Candidates with these skills are more likely to come from a social science background because in the medical field, sociology, economics, or geography they’re accustomed to doing this with their data.
Hire for talent, train for tech skills
The second thing to do is to hire for talent and train for tech skills. This is really important because…
Analytical thinking and communication skills are harder to teach than SQL, Python and R.
I came from an academic background, and while I used data for statistical analysis, I had very little experience doing any kind of coding. Luckily the people who hired me for my first tech job gave me a take home test and I was able to Google all of the coding that I needed to write a beautiful report. Of course when I went in for the on site interview, I completely bombed the technical side of the interview. But they thought I was pretty smart and decided to hire me anyway. And I was able to pick up the SQL and Python through their training and mentorship.
The added benefit of training for tech skills, is that you create an environment where mentorship is really strong, which creates a more cohesive team.
Broaden your pool of candidates
We have some really strong Data Science candidates here already in the tech world and also graduating from Berkeley and Stanford. But if you look just a little bit deeper than that, or a little bit broader than that, there are a lot of would be Data Scientists at State schools and places like Zipfian and other data boot camps that have popped up.
Think outside the box when looking for candidates
The other benefit of this approach, is that if you’re looking in a broader pool of candidates, you’re also going to increase diversity in your company. And by diversity I also mean geography and age, not just gender and race.
Diversity in the workplace is really important because research has shown that it increases your revenue by promoting innovation and creative thinking. Diversity is a huge initiative right now at Google where 100% of managers and 25% of their employee base is going through unconscious bias training.
Hopefully these insights and tips will help you build a great Data Science team!
About the author
Sara Vera is a Data Scientist at Insightly, a customer relationship management tool for a small business. It’s a simple, yet powerful, CRM system for small businesses with integrations to Google Apps, Office 365, MailChimp, and major social media sites.
What is Extract?
Extract is one full day jam-packed with data stories that will entertain, educate and inspire you. It’s everything you’ve ever wanted to know about data, told by the people who know it best. Our speakers hail from some of the most successful and innovative companies in the business. You’ll hear data-driven talks on everything from beating the competition to creating the next unicorn. And our workshops will showcase the best of the best in data tooling. You’ll get an exclusive look at some of the latest technologies and pick up first-hand tips on implementing new strategies.