Job Description

Meta is seeking a Research Scientist to join the Quantitative Growth Research team, where you will apply rigorous quantitative methods to understand user behavior, product adoption, and growth dynamics across Meta's family of apps and services. In this role, you will design and execute large-scale studies, develop causal inference frameworks, and translate complex behavioral data into actionable insights that shape product strategy and drive sustainable growth. You will partner closely with product, engineering, and data science teams to define research agendas that directly influence how billions of people discover and engage with Meta's products.

Responsibilities
Design and execute quantitative research studies — including surveys, behavioral data analyses, and observational studies — to understand user acquisition, retention, and engagement patterns across Meta's products
* Develop and apply causal inference methods (e.g., difference-in-differences, instrumental variables, regression discontinuity) to evaluate the impact of product changes on growth outcomes
* Build and validate measurement frameworks that connect user behavior signals to long-term growth and retention metrics
* Collaborate with product managers, engineers, and data scientists to translate research findings into product strategy and roadmap decisions
* Identify gaps in existing measurement approaches and design novel methodologies to address them, adapting established frameworks to new growth research challenges
* Communicate complex quantitative findings to technical and non-technical stakeholders through written reports, presentations, and data visualizations
* Contribute to team-level research goals by identifying high-priority growth questions and scoping research projects with clear short, mid, and long-term deliverables
* Leverage AI-integrated workflows and analytical tools to accelerate research design, data processing, and insight generation
* Advise cross-functional partners on appropriate research methods, statistical interpretation, and the limitations of quantitative findings in the context of growth decisions

Qualifications
PhD in a quantitative field such as economics, statistics, psychology, computational social science, or a related discipline
* Experience conducting quantitative research using methods such as experimentation, causal inference, survey research, or large-scale behavioral data analysis
* Experience applying statistical modeling techniques (e.g., regression, survival analysis, Bayesian methods, causal inference) to real-world product or growth research questions
* Experience translating quantitative research findings into product or business recommendations for cross-functional stakeholders
* Experience working with large-scale datasets using programming languages such as Python, R, or SQL
* Experience scoping and delivering independent research projects from problem definition through insight communication Familiarity with survey methodology, including questionnaire design, sampling strategies, and measurement validity in the context of user research
* Experience designing and analyzing large-scale experiments (A/B tests or quasi-experiments) in a consumer technology or growth context
* Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
* Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
* Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
* Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
* 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 optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
* Track record of publishing or presenting quantitative research findings in peer-reviewed venues or applied research settings