This was contributed by Anirban Das. He is a Data Scientist at Niara
If you search on LinkedIn for data-driven large companies like Amazon, Netflix, Facebook, Uber, Airbnb, you will find that they employ plenty of data scientists. The scenario is significantly different for older public technology companies in the Bay Area and many older private companies which are likely to go public. In terms of percentage of employees, a smaller start-up will often have more data scientists than one of these older companies. Consequently many of these large companies feel the peer pressure to hire data scientists, often without knowing much about the role, the value the person can bring, the problem they can solve or how machine learning works. You will often find them posting a data scientist/senior data scientist position when there isn’t any senior member in the team who is actually qualified to evaluate the applicants. There is nothing more frustrating than reporting to a manager who doesn’t understand your work.
Given the scenario, the best approach for a large company with no data scientists is to hire the Director of Data Science before hiring their first data scientist. This person would be someone with a PhD in a quantitative field or bring strong research experience (in absence of PhD) and 7-10 years of professional/academia experience with a proven track record. This would be the person who can develop the analytics capability that can boost the company’s business and propose data driven products and solutions. I understand that hiring a Director of Data Science can be significantly more expensive and challenging than hiring a senior data scientist but thinking long-term is a better approach. Amazon, Netflix, Uber are some good examples of how successful companies build robust data science teams that got desired results.
Now comes the difficult part — finding the right candidate. Already it isn’t easy to hire a senior data scientist or principal data scientist. The challenge is even higher when trying to fill director level positions. Fortunately there is a solution, that is to look beyond candidates that are already working in the industry — academia can be a great source for hiring director level data scientists. There are several reputed faculty members and researchers in machine learning who are currently working in academia. Hiring a brilliant scientist as your Director of Data Science could be the key to transforming a large business to a successful data-driven organization. Industry working with academia isn’t new, several reputed companies have done it. Uber hired talent out of CMU to help them build the self driving car. nuTonomy, a company that launched world’s first self driving cab in Singapore, is a spin-off from MIT, and Apple recently hired a CMU professor as their director of AI research. It is time we get over the bias that industry has toward academia, and start thinking creatively to solve the problem related to hiring high-demand roles.
Disclaimer: The views expressed here are my own and don’t represent the views of my employer.
