Research Scientist

Meta Meta · Big Tech · San Francisco, CA

Research Scientist at Meta focused on developing and implementing novel quantitative and machine learning methods to answer product questions and generate insights. The role involves working with large datasets, applying advanced ML techniques like Bayesian modeling, reinforcement learning, and causal inference, and optimizing neural network models. The scientist will also draft software from scratch to implement these methods and collaborate with cross-functional teams.

What you'd actually do

  1. Work closely with a Product Engineering team to identify and answer important product questions using scientific techniques and research methods.
  2. Develop novel quantitative methods on top of Meta's unparalleled data infrastructure.
  3. Answer product questions and generate insights by using appropriate machine learning, statistical techniques or other relevant scientific modeling approaches on available data.
  4. Draft software from scratch to implement novel methods.
  5. Build cross-functional partnerships throughout Meta.

Skills

Required

  • Bachelor's degree (or foreign degree equivalent) in Computer Science, Computer Engineering, Data Science or related field
  • 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

Nice to have

  • Draft software from scratch

What the JD emphasized

  • Solving analytical problems using quantitative approaches including statistical analysis, hypothesis testing, supervised and unsupervised modeling
  • 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

Other signals

  • Develop novel quantitative methods
  • implement novel methods
  • Apply methods for sequential decision-making under uncertainty
  • Optimizing the architecture and training parameters of Neural Network (NN) models