1.1.3.5 Understanding roles and titles

While role definitions are useful for career orientation, a role definition poorly reflects what you do on the job. Two people with the same title on the same team might do very different things. Two people doing similar things at different companies might have different titles. In 2018, Lyft decided to rename their role of “Data Analyst” to “Data Scientist”, and “Data Scientist” to “Research Scientist”¹³, a move likely motivated by the job market’s demands, which shows how interchangeable roles are.

🌳 Tip 🌳
When unsure what the job entails, ask. Here are some questions that might help you understand the scope of a role you're applying for.

• How much of the job involves developing ML models?
• How much of the job involves data exploration and data wrangling? What are the characteristics of the data you'd have to work with, e.g. size, format?
• How much of the job involves DevOps?
• Does the job involve working with clients/customers? If yes, what kind of clients/customers? How many would you need to talk to? How often?
• Does the job involve reading and/or writing research papers?
• What are some of the tools that the team can't work without?

In this book, I use the term “machine learning engineer” as an umbrella term to include research engineer, devrel engineer, framework engineer, data scientist, and the generic ML engineer.

🌊 Resources 🌊

• What machine learning role is right for you? by Josh Tobin, Full Stack Deep Learning Bootcamp 2019.
• Data science is different now by Vicki Boykis, 2019.
• The two sides of Getting a Job as a Data Scientist by Favio Vázquez, 2018.
• Goals and different roles in the Data Science platform at Netflix by Julie Pitt, live doc.
• Unpopular Opinion - Data Scientists Should Be More End-to-End by Eugene Yan, 2020.

¹³: What’s in a name? The semantics of Science at Lyft by Nicholas Chamandy (Lyft Engineering blog, 2018)