Analyzing the Analyzers

Chapter 1. Introduction

Binita, Chao, Dmitri, and Rebecca are data scientists. What does that statement tell you about them? Probably not as much as you’d like. You know they probably know something about statistics, programming, and data visualization. You’d hope that they had some experience finding insights from data, maybe even “big data.” But if you’re trying to find the best person for a job, you need to be more specific than just “doctor,” or “athlete,” or “data scientist.” And that’s a problem. Finding the right people for a task is all about efficient communication and, without the appropriate shared vocabulary, data science talent and data science problems are too often kept apart.

The three of us, organizers of data science events in Washington, DC, decided that we wanted to do something about this problem after too many personal experiences of failures caused by miscommunication. So in mid-2012 we surveyed data scientists, asking about their experiences and how they viewed their own skills and careers. The results may help us, as a professional community, settle on finer-grained descriptions and more effective means of communicating about what we do for a living.

We start by describing four fictitious data scientists, each typical of one of four categories that emerged from the survey. Their variety is striking.

Binita works for Acme Industries — a Fortune 100 manufacturing company — as Director of Analytics. She manages a small team of technical analysts and spends rather ...