You are viewing a preview of this job. Log in or register to view more details about this job.

Machine Learning Data Scientist Intern, Infrastructure Data Center (PhD)

As we plan to grow our Data Center organization, we need to ​leverage data to drive decisions and ​introduce automation, predictability & optimization to allow us to operate ​efficiently and reliably at scale. Our data scientist team identifies business problems and solves them by using various numerical techniques, algorithms, and models in Operations Research, Data Science, and Data Mining. In this role, your primary responsibility will be to partner with key stakeholders and lead the development of an analytics program to support and enable the continued growth critical to Meta's Data Center organization. You will have the opportunity to work on a broad spectrum of areas such as Supply Chain Optimization, Inventory & Capacity Planning, Process Design & Optimization, Financial Modeling and Demand Forecasting.

The ideal candidate will have a passion for working in white space and creating impact from the ground up in a fast-paced environment. Additionally, you will have a proven track record of thought leadership and impact in developing similar analytics and metrics based programs.

This is a 12-week internship supporting our Infrastructure Data Center team and located in Fremont, CA.

Machine Learning Data Scientist Intern, Infrastructure Data Center Responsibilities
  • Apply Machine Learning (ML), statistical modeling, & AI algorithms to large-scale distributed systems, forecasting systems and scheduling systems.
  • Build pragmatic, scalable, and statistically rigorous solutions for data center infrastructure problems by leveraging or developing state-of-the-art machine learning methodologies on top of Facebook's unparalleled data infrastructure.
  • Partner with internal stakeholders on projects to identify and articulate opportunities, see beyond the data to identify solutions that will raise the bar for decision making.
  • Collaborate with cross-functional data and product teams across business applications to access and manipulate data, explain data gathering requirements, display results, and build efficient and scalable analytics solutions.
  • Recommend process changes based on robust analysis of operational data and user behavior to improve overall business performance.
  • Communicate final recommendations and drive decision making.

Minimum Qualifications
  • Pursuing MS or PhD in quantitative field such as Operations Research, Computer Science, Quantitative Finance, Math, Physics or a related Engineering degree
  • Knowledge of Statistics & Probability (e.g. Hypothesis testing, Regression)
  • Familiar with SQL. Ability to program in one programming language (Python or C++)
  • Experience in modeling and analyzing large-scale structured and unstructured data using Python or R.
  • Knowledge of Machine learning and Deep learning algorithms.
  • Experience in building machine learning models and new frameworks in academia and/or industry

Preferred Qualifications
  • Currently has, or is in the process of obtaining a MS degree or PhD in an analytical field (e.g. Computer Science, Engineering, Mathematics, Statistics, Operations Research, Management Science).
  • Deep expertise in one of the areas - Graph Learning, NLP, Time series models or Deep learning methods.
  • Familiarity with visualization tools (such as Tableau).
  • Experience writing testable code and shipping code into production.
  • Hands-on experience working on operational problems in an industrial environment.