How to Hire a Chief Data Scientist


How do you hire a Chief Data Scientist? That's not a hypothetical question: I know of at least three companies that are actively looking for a "Chief Data Scientist" at the moment. Hiring the right person is harder than you'd think.

Whether or not a Chief Data Scientist is a necessary role for every organisation out there is an interesting question but arguably irrelevant - in terms of revealed preference, it's enough that they're recruiting.

Hiring a Chief Data Scientist opens up a whole nest of problems. The obvious one is structural: there's just not enough people to go around. For those who are competitive and looking for a challenge, it really is a world market at the moment. Since looking locally rarely yields enough candidates given the dearth of the pool, it almost inevitably ends up being a global search as well.

There is a subtler issue: defining the role is hard! After all, it's not like there's a library of position descriptions out there. And, there's surprisingly little consensus about what the job should involve - ask five people what they think makes a good data scientist and you'll probably get seven answers!

The obvious is to assume that it's a like a data scientist, just smarter. This would suggest one (or more) PhDs in technical areas, deep experience in "bare metal" algorithm development (in MapReduce, C, or any other often low-level language), and a demonstrated history of applied mathematics in a business or commercial context.

I think this is a mistake. I'm not alone, either: management is always different from application and analytics is no different. Information technology and marketing moved through similar transitions as they gravitated away from finance and sales.

Today's Chief Information Officer and Chief Marketing Officer need more than just technical skills. Anyone who suggests with a straight face that the main measure of an effective Fortune 100 CIO is their ability to cut code will probably get laughed out of the building. The same goes for the CMO – the ability to develop creative content is important but it pales in comparison to driving marketing return on investment.

From where I sit, the ideal Chief Data Scientist blends three different competencies:

  • Technical skills
  • Value creating skills
  • Change management skills

Technical skills are an essential part of the job. It's hard to be credible in the field if one can't spell heteroscedasticity off the top of one's head. However, breadth of knowledge is arguably more important than depth in this space.

Depth is great when every problem is the same. While there are a wide variety of problems that benefit from common competencies, the set of problems that do not benefit from common competencies is a larger one. Besides, it's relatively easier (and cheaper) to hire smart people fresh from university or consultancies. Why pay top dollar at the C-level when there's a hoard of PhD graduates who would leap at a (proportionally tiny) offer 10% higher than market rates?

Arguably more important than technical knowledge is the ability to channel that knowledge into value-creating initiatives. Getting past insight is a hard lesson to learn: it may be a great model but if it doesn't add anything to the bottom line or to social outcomes, no-one's going to care. This awareness isn't cheap, either. Anyone who's deployed operational analytics and analytically-driven microdecisions has more than a few battle-scars and stories to talk about.

Finally, and most importantly, the job is about leading and managing change. The heart of business analytics isn't maths. It's about getting everyone to behave differently based on data-driven insight.

Analytics helps identify how things could be better. Business analytics is about convincing everyone that it's worth doing things differently. Change management and persuasion are the two most important skills in the field and yet frequently paid the least attention. An effective Chief Data Scientist needs to live them, heart and soul; if they don't, who will?

So how do you hire the ideal Chief Data Scientist? Here are my tips:

  • Look for people with a demonstrated history of driving change. They're going to need to convince the organisation to behave differently, something that's notoriously hard.
  • Grill them on their technical chops. Or, even better, get your most technical analytical people to do it for you. It's a technical discipline and there's more than a few people out there happy to take advantage of information asymmetries if it means they'll get a nice title and a fat pay check.
  • Communication is an essential part of the job. So is innovation. Use social media to look for people with a broad and diverse network and then test them on their implied relationships. On one hand, it's hard to build a real network without some social mores. On the other, innovation is easier when it draws from fresh exposure to fresh ideas.
  • Make sure they understand what you're looking for and that you know what they're looking for. If you're after a change agent, getting someone who just wants to patent new ideas may end up in buyer's remorse.

What do you suggest?


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