Rarefied talent in data science, data technology, and analytics

Quantitative Engineer, Business Intelligence


Job Description

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 business problems and asking the right questions to develop viable solutions with a focus on sales, marketing and finance. 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, 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: Solid understanding of fundamentals of statistics is a must; expertise with time series analysis and/or machine learning techniques very welcome. As for the technology stack, deep knowledge of (or desire to swiftly learn) Python in the context of scientific and general coding, some R and some SQL to work with Hive 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 analyst and a generalist software engineer.

This position is full-time and located in our Menlo Park office.


  • Work closely with Sales, Marketing, Finance, Monetization and Product teams to provide impactful analysis and insights

  • Drive the collection of new data and the refinement of existing data sources

  • Apply known statistical and machine learning techniques and devise new ones to understand and analyze data

  • Implement quantitative methodologies in high-quality code using software engineering best practices

  • Communicate findings via reports and presentations to internal and external audiences of all levels


  • MS/PhD in computer science, computational statistics, computational econometrics, operations research or related field

  • Hands-on, deep knowledge of Python as a user of scientific libraries and as a generalist. Alternatively, R or MATLAB with strong C++ or Java experience

  • Excellent understanding of fundamentals of statistics

  • Familiarity with time series analysis and forecasting techniques, previous hands-on experience a big plus

  • Familiarity with machine learning techniques, previous hands-on experience a big plus

  • Familiarity with SQL, large datasets (>1TB) and distributed computing a plus

Interested in this position?
Job Location
1 Hacker Way
Menlo Park, CA 94025
Additional Job Details
Employment Type:
Full Time