Core Data Science (CDS) is a research and development team, working to improve Meta's products, infrastructure, and processes. We generate real-world impact through a combination of scientific rigor and methodological innovation. Our focus is on longer-term, foundational work that addresses new opportunities and challenges across the Meta family of apps. The work we do enhances the Meta family of apps that enable billions of people to communicate with each other daily. Core Data Science is interdisciplinary, with expertise in computer science, statistics, machine learning, economics, political science, operations research, and sociology, among many other fields. This diversity of perspectives enriches our research and expands the scope and scale of projects we can address. We deliver value through collaborative projects with other groups at Meta and with the academic community. In addition, we build and open-source technical products aligned with our areas of expertise. We are looking for research interns to join the Product Algorithms team in Core Data Science. Core Data Science is an interdisciplinary team of quantitative scientists that aims to deliver research and innovation that fundamentally increase the magnitude of Facebook's successes. By applying your expertise in quantitative methods, you will be empowered to drive impact across a range of products, infrastructure and operational use cases at Facebook. Our internships are twelve (12) - twenty four (24) weeks long and have various start dates throughout the year.
Research Scientist Intern, Product Algorithm Research (PhD) Responsibilities:
Work closely with a product engineering team to identify and answer important product questions
Answer product questions by using appropriate statistical techniques on available data
Communicate findings to product managers and engineers
Drive the collection of new data and the refinement of existing data sources
Analyze and interpret the results of product experiments
Develop best practices for instrumentation and experimentation and communicate those to product engineering team
Minimum Qualifications:
Currently has, or is in the process of obtaining, a PhD degree in the field of Computer Science or related technical field, with emphasis in machine learning, recommender systems, statistics, algorithms, and optimization.
2+ years experience manipulating and analyzing complex, high-dimensionality large scale data in Python or R
Past experience communicating complex quantitative analysis in a clear, precise, and actionable manner
Proven track record of solving difficult analytical problems using quantitative approaches Experience using machine learning and deep learning frameworks, such as PyTorch, TensorFlow or scikit-learn
Experience working with large data sets, experience working with distributed computing tools a plus (Map/Reduce, Hadoop, Hive, etc.)
Experience working with large data sets
Must obtain work authorization in country of employment at the time of hire, and maintain ongoing work authorization during employment
Preferred Qualifications:
Experience working with distributed computing tools such as Map/Reduce, Hadoop, Hive, or similar
Proven track record of achieving significant results as demonstrated by grants, fellowships, patents, as well as first-authored publications at leading workshops or conferences such as KDD, WWW, WSDM or AAAI
Intent to return to degree-program after the completion of the internship/co-op
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