Quantitative Engineer, Business Intelligence
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.
Responsibilities
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
Requirements
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
Menlo Park, CA 94025
Full Time