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. To apply, click "Apply to Job" online on this web page.

Research Data Scientist Responsibilities

• Build pragmatic, scalable, and statistically rigorous solutions to large-scale web, mobile and data infrastructure problems by leveraging or developing state-of-the-art statistical and machine learning methodologies on top of Meta's unparalleled data infrastructure.


• Work cross-functionally to define problem statements, collect data, build analytical models and make recommendations.


• Build and maintain data driven optimization models, experiments, forecasting algorithms, and machine learning models.


• Leverage tools like Python, R, Hadoop and SQL to drive efficient analytics.


• Communicate final recommendations and drive decision making.

Minimum Qualifications

• Requires a Doctor of Philosophy in Computer Science, Statistics, Mathematics, Physics, Geophysics, or a related field. Foreign equivalent accepted. Requires 24 months of experience in the job offered or in a related occupation. Experience must include 24 months involving the following:


• 1. Solving analytical problems and building models using quantitative, statistical or machine learning approaches


• 2. Machine Learning, Statistics, or other data analysis tools and techniques


• 3. Performing data extraction, cleaning, analysis and presentation for medium to large datasets


• 4. Programming language: Python, R, Java, or C++


• 5. Writing SQL queries


• 6. Scientific computing and analysis packages, including NumPy, SciPy, Pandas, Scikit-learn, or dplyr


• 7. Statistics methods such as forecasting, time series, hypothesis testing, classification, clustering or regression analysis


• 8. Data visualization libraries, including Matplotlib, Pyplot, or ggplot2.