Data Scientist, Product Analytics

Meta•Menlo Park, CA

1d•$147,000 - $208,000

About The Position

Meta is seeking a Data Scientist to drive product strategy and decision-making across our family of applications, including Facebook, Instagram, Messenger, WhatsApp, and Meta's emerging platforms. In this role, you will apply advanced quantitative analysis, experimentation, and data storytelling to one of the world's richest data sets, helping shape experiences for billions of people and hundreds of millions of businesses. You will partner closely with Product, Engineering, Research, and cross-functional teams to identify opportunities, measure impact, and translate complex data into clear, actionable recommendations that influence product roadmaps and investment decisions.

Requirements

Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
6+ years of experience in product analytics, data science, or a related quantitative field
6+ years of experience with data querying languages such as SQL and scripting languages such as Python or R for data analysis and modeling
6+ years of experience applying quantitative approaches including statistical analysis, experimentation design, and predictive modeling to solve product problems
Experience designing and evaluating A/B tests and other causal inference methodologies to measure product impact
Experience communicating complex analytical findings and data-driven recommendations to technical and non-technical stakeholders through written reports and data visualizations

Nice To Haves

Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
Experience applying causal inference techniques beyond standard A/B testing, such as difference-in-differences, synthetic control, or instrumental variables
Experience building self-service dashboards and interactive data visualization tools that enable product and engineering teams to explore data independently
Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
Experience with forecasting methods such as time series analysis applied to product metrics at scale
Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
Demonstrated ability to integrate AI tools to redesign analytics workflows and deliver measurable improvements in efficiency or analytical quality

Responsibilities

Work with large, complex data sets to solve challenging product problems using quantitative analysis, statistical modeling, and data mining techniques
Design and evaluate experiments to measure the impact of product changes, ensuring rigorous hypothesis formulation, appropriate methodology selection, and sound interpretation of results
Identify, define, and monitor key product metrics to track performance trends, detect anomalies, and inform product health assessments
Build and maintain scalable data pipelines, staging solutions, and self-service visualization interfaces that enable cross-functional partners to explore product data independently
Develop forecasting models and predictive analyses to size opportunities, anticipate product challenges, and support goal-setting processes
Translate analytical findings into clear, concise narratives and data-driven recommendations tailored to product and engineering stakeholders
Collaborate with Product and Engineering teams to define success metrics, shape product roadmaps, and prioritize initiatives based on measured impact
Advise cross-functional partners on analytical design, research methodology, and best practices for data collection and interpretation
Integrate AI tools systematically into analytics workflows to improve efficiency, expand analytical scope, and deliver higher-quality insights at scale
Contribute to team-level goal setting by identifying and sizing emerging product opportunities and translating them into measurable objectives