Machine Learning in Production: Developing and Optimizing Data Science Workflows and Applications

1. The Role of the Data Scientist

1.1 Introduction

We want to set the context for this book by exposing you to the focus on products, rather than methods, early on. Data scientists often make shortcuts, use rules of thumb, and forgo rigor. They do this in favor of speed and with reasonable levels of uncertainty with which to make decisions. The world moves fast, and businesses don’t have time for you to write a dissertation on error bars when they need answers to hard questions.

We’ll begin by describing how the sizes of companies put different demands on a data scientist. Then, we’ll describe agile development: the framework for building products that keeps them responsive to the world outside of the office. We’ll discuss ladders and career ...