Careers at Facebook

Combine one of the largest and richest datasets in the world with some of the most powerful and sophisticated computing systems available today and you get a day in life of a Quantitative Engineer at Facebook.

We start by identifying high-impact problems and asking the right questions to develop viable solutions with a focus on the business side of Facebook. We collect the necessary data (lots of it!) to answer these questions and apply known analysis and modeling techniques (and sometimes devise new ones) to this data to get answers. We often work with sales, marketing, finance, people, monetization and product teams to refine our approach. Once we get repeatable answers, we not only produce reports and presentations but also develop software frameworks and tools so that the next time we have a similar question, we don't have to work as hard to get an answer. Yes, we are lazy that way ;-).

The role asks for the usual suspects in terms of quantitative know-how: Good understanding of fundamentals of statistics and expertise with various machine learning techniques are must-haves. As for the technology stack, deep knowledge of (or desire to swiftly learn) Python in the context of scientific coding and distributed computing and some SQL are pretty much required to get anything done. As you can tell, we expect Quantitative Engineers to be highly data-driven and quite comfortable working both as a quantitative scientist and a generalist software engineer.