Core Data Science (https://research.fb.com/core-data-science/) is a research and development team, working to improve Facebook’s products, infrastructure, and processes. We generate real-world impact through a combination of scientific rigor and methodological innovation. We are an interdisciplinary team, with expertise in computer science, statistics, machine learning, economics, political science, operations research, and computational social sciences, among other fields. This diversity of perspectives enriches our research and expands the scope and scale of projects we can address.
We are looking for researchers and data scientists to join the team in Tel Aviv. We work closely with various product groups throughout Facebook, bringing expertise in machine learning, statistics, causal inference, data analysis, and other quantitative methods. By applying your expertise in such topics you will be empowered to drive impact across a range of products, infrastructure and company operations. The ideal candidate will have a passion for building products and applying research expertise and the latest methods to solve challenging, real-world problems.
- Build data driven solutions to mission critical inferential and decision problems by developing state of the art statistical and machine learning methods on top of Facebook's unparalleled data infrastructure.
- Communicate best practices in quantitative analysis and develop cross-functional partnerships throughout the company.
- Work both independently and collaboratively with other scientists, engineers, designers, UX researchers, and product managers to accomplish complex tasks that deliver demonstrable value.
- Actively identify new opportunities within Facebook's long term roadmap for data science contributions.
- PhD in computer science, statistics, economics, or related quantitative field, or MS degree with 4+ years of relevant experience.
- Experience using machine learning and statistical analysis for building data-driven product solutions or performing methodological research.
- Experience in programming and data analysis using languages such as R or Python, with packages such as NumPy, SciPy, pandas, scikit-learn, tidyverse (dplyr, ggplot2, etc.).
- Ability to initiate and drive research projects to completion with minimal guidance.
- The ability to communicate scientific work in a clear and effective manner.
- Publications in Machine Learning, statistics, data science, AI, computer science, or related technical fields
- Experience in production level software development in Python or lower level languages such as C++, Java.
- Experience in scalable dataset assembly / data wrangling, such as Presto, Hive or Spark.