Central Applied Science (CAS) is looking to improve Meta's products, infrastructure, and processes by leveraging scientific rigor and methodological innovation, focusing on longer-term, foundational work that addresses new opportunities and challenges across the Meta family of apps. The Graph Science and Statistics team specifically aims to solve critical problems that enable Meta to make robust and trustworthy strategic decisions and build products that best serve and protect their users.
Requirements
- Experience in machine learning and statistical research covering at least one of the following domains: graph and sequence modeling, statistical modeling, network science, marketing science, relational learning, user modeling, model evaluation or generative AI
- 1+ years in programming, analysis, and visualization using computing software and libraries such as Python or R
- Experience in scalable dataset assembly/data wrangling, such as Hive, Spark or Presto
- PhD degree in a quantitative field such as Statistics, Computer Science, Information Science, Data Science, Quantitative Marketing or relevant technical field
- Proven track record of achieving significant results as demonstrated by previous internships or industry experience, fellowships, patents, as well as first-authored publications at leading workshops or conferences such as KDD, The Web, AI STATS, AAAI, NeurIPS, ICML, or similar
Responsibilities
- Build pragmatic, scalable, and statistically rigorous solutions to mission critical inferential and decision problems by leveraging or developing state of the art statistical and machine learning methodologies on top of Meta’s unparalleled data infrastructure.
- Apply excellent communication skills in order to develop cross-functional partnerships throughout the company and spread statistical best practices.
- Be able to work both independently and collaboratively with other scientists, engineers, designers, UX researchers, and product managers to accomplish complex tasks that deliver demonstrable value to Meta’s community of over 3.5 billion users.
- Think creatively, proactively, and futuristically to identify new opportunities within Meta’s long term roadmap for data-scientific contributions.
- Leveraging or developing state of the art statistical and machine learning methodologies
- Statistical methodology for model evaluation and bias correction
- Graph and representation learning, and GenAI model development and application
Other
- Currently has, or is in the process of obtaining, a PhD degree
- Interpersonal experience: cross-group and cross-culture collaboration
- Must obtain work authorization in the country of employment at the time of hire, and maintain ongoing work authorization during employment
- Intent to return to degree-program after the completion of the internship/co-op
- Experience working and communicating cross functionally in a team environment
