Data Scientist, Product Analytics | Scientifique de données en analyse de produits

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 modeling to one of the world's richest data sets, uncovering insights that shape product development for billions of people and hundreds of millions of businesses. You will serve as a strategic analytics partner to Product and Engineering teams, translating complex data into clear narratives that influence roadmap priorities, goal setting, and investment decisions. You will operate with significant autonomy, leading end-to-end analytics projects and advising cross-functional partners on measurement frameworks, opportunity sizing, and product performance.

Requirements

6+ years of experience in product analytics, data science, or a related quantitative field, applying statistical and analytical methods to product or business problems
6+ years of experience querying and analyzing large data sets using SQL, with proficiency in a scripting language such as Python or R for statistical analysis and data manipulation
6+ years of experience designing and interpreting experiments, including A/B testing and causal inference methods, to measure the impact of product changes
Experience developing forecasting or predictive models and translating outputs into actionable product or business recommendations
Experience communicating complex analytical findings to technical and non-technical stakeholders through written narratives, data visualizations, and structured presentations

Nice To Haves

Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
Experience applying causal inference techniques beyond standard A/B testing, such as difference-in-differences, instrumental variables, or synthetic control methods
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)
Experience working on consumer-facing products at scale, with familiarity in modeling user behavior, retention, engagement, or monetization metrics
Demonstrated ability to integrate AI tools to redesign analytics workflows and deliver measurable improvements in efficiency or analytical scope
Experience building generalized data staging pipelines or self-service visualization interfaces that support multiple product or business use cases

Responsibilities

Partner with Product and Engineering teams to define success metrics, build measurement frameworks, and evaluate the impact of product changes through rigorous experimentation and A/B testing
Analyze large, complex data sets using SQL and Python to identify trends in user behavior, product engagement, and ecosystem health across Meta's family of applications
Design and execute end-to-end quantitative research projects, from hypothesis formulation and data collection strategy through analysis, insight generation, and stakeholder communication
Develop and maintain forecasting models and predictive analyses to anticipate product performance trends and inform long-term planning
Build scalable self-service data visualizations and dashboards that enable cross-functional partners to explore product metrics and monitor operational performance
Identify and size new product opportunities by synthesizing performance data across multiple signals and relating findings to team-level goals and business priorities
Advise cross-functional partners on analytical design, statistical methodology, and interpretation of results to ensure rigor and consistency across product analytics work
Translate analytical findings into clear, concise narratives and data-driven recommendations that influence product strategy and investment decisions at the team level
Integrate AI tools into analytics workflows to accelerate insight generation, improve analytical quality, and expand the scope of problems the team can address
Contribute to team-level goal setting by identifying short, mid, and long-term metrics that reflect product health and align to broader business objectives