Analyzing the Analyzers

Chapter 2. Case Studies in Miscommunication

There are two related issues that we have seen when it comes to misunderstandings about the roles of data scientists. In one case, excessive hype leads people to expect miracles, and miracle-workers. In the other case, a lack of awareness about the variety of data scientists leads organizations to waste effort when trying to find talent. These case studies are based on collective experiences from many of our friends, colleagues, and Meetup members.

Rock Stars and Gods

Dmitri, our machine learning developer, gets head-hunted by a successful e-commerce company that has now realized the need for a data scientist. The recruiter supplies the following job description:

We’re looking for a Data Scientist Superstar to revolutionize the online experience. Are you excited about leveraging state-of-the-art methods to turn big data into business value? Can you manage people and projects and see an idea through from conception to delivery?

The rest of the job description shows a laundry list of desired skills, including terms like “Big Data,” a dozen algorithm names, and other jargon. Dmitri thinks that this looks pretty reasonable and is comfortable that he meets the vast majority of the requirements. He has several phone interviews and is invited onsite.

During the first few minutes of Dimitri’s meeting with the CEO, it becomes clear that they are looking for much more than what was previously discussed. Dmitri asks politely for more details about what ...