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Meta Platforms, Inc. (Meta), formerly known as Facebook Inc., builds technologies that help people connect, find communities, and grow businesses. When Facebook launched in 2004, it changed the way people connect. Apps and services like Messenger, Instagram, and WhatsApp further empowered billions around the world. Now, Meta is moving beyond 2D screens toward immersive experiences like augmented and virtual reality to help build the next evolution in social technology.
Job Responsibility:
Work closely with a Product Engineering team to identify and answer important product questions using scientific techniques and research methods
Develop novel quantitative methods on top of Meta's unparalleled data infrastructure
Answer product questions and generate insights by using appropriate machine learning, statistical techniques or other relevant scientific modeling approaches on available data
Draft software from scratch to implement novel methods
Build cross-functional partnerships throughout Meta
Communicate best practices in quantitative analysis to partners
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 users
Drive the collection of new data and the refinement of existing data sources
Identify new opportunities within Meta's long-term roadmap for scientific contributions
Requirements:
Bachelor's degree (or foreign degree equivalent) in Computer Science, Computer Engineering, Data Science or related field
Requires completion of an undergraduate-level course, research project, or internship involving the following: Solving analytical problems using quantitative approaches including statistical analysis, hypothesis testing, supervised and unsupervised modeling
Manipulating and analyzing data from varying sources using programming languages such as Python, data manipulation tools like Pandas and NumPy, database querying languages such as SQL, and visualization tools such as Matplotlib
Communicating quantitative analysis including presentation of complex statistical concepts and results, writing of clear and actionable reports and dashboards, and visualization of data insights
Leveraging probabilistic concepts such as statistical distributions, hypothesis testing, expectations, and estimators
Familiarity with efficient data structures such as arrays, lists, trees, graphs, and maps
Experience leveraging advanced ML techniques such as Bayesian modeling, reinforcement learning, bandits, and causal inference
Applying methods for sequential decision-making under uncertainty, including Markov Decision Processes (MDPs) and Reinforcement Learning (RL)
Optimizing the architecture and training parameters of Neural Network (NN) models