data scientists are one of the most sought after roles in today's technology organizations. Salaries are rising for this important role. In some IT organizations, CIOs may only have the budget to hire one data scientist. So when it comes time to hire a data scientist you probably want that person to be a rockstar.
But when you're doing your interviewing, there's one key skill that you should hone in on that may not be obvious on the resumes your vetting. It isn’t Python or R or Spark or some other new technology or platform. It isn’t the latest machine learning methods or algorithms. It isn’t being able to write AI algorithms from scratch or analyze terabytes of data in minutes.
While those are important – very important – they aren’t THE skill. In fact, the one skill that makes a data science rockstar isn't a technical skill at all – it's a so-called “soft-skill:” The ability to communicate.
The candidates you're interviewing could be the smartest people in the world when it comes to creating some wild machine learning systems to build recommendation engines, but if they can’t communicate the “strategy” behind the system or their approach, they're going to have a hard time, and their potential is going to be unrealized.
What do I mean by “strategy?” When you communicate your output/results, data scientists need to be able to discuss more than the standard information (error rates/metrics, etc.). They also need to be able to hit the key "W" points: What, why, when, where, and who. They must be able to clearly define what they did, why they did it, when their approach works (and doesn’t work), where their data came from and who will be affected by what they’ve done. If they can’t answer these questions succinctly and in a manner that a layperson can understand, they’re a failing a data scientist.
I have two recent examples for you to help highlight the difference between a data science rockstar (i.e., someone that communicates well) and one not-so-much rockstar. I’ll give you the background on both and let you make up your own mind on which person you’d hire as your next data scientist. Both of these people work at the same organization.
She’s been a data scientist for four years. She’s got a wide swath of experience in data exploration, feature engineering, machine learning, and data management. She’s had multiple projects over her career that required a deep dive into large datasets and she’s had to use different systems, platforms and languages during her analysis.
For each project she works on, she keeps a running notebook with commentary, ideas, changes and reasons for doing what she’s doing – she is a scientist after all. When she provides updates to team members and management, she doesn’t just focus on the data, she focuses on what the data is able to communicate.
