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Meta Platforms · Sunnyvale, CA +4 more

Data Scientist, Analytics (Ranking, AI)

Sunnyvale, CA +4 moreRemoteData & Analytics

Listed on Meta Platforms’s official careers site. Listed as of October 18, 2026. Check that it is still open and apply there.

Apply on Meta Platforms’s careers site →

Job description

Ranking AI is the central core machine learning org within Meta’s Monetization group, powering Meta’s revenue and business growth by advancing and deploying state-of-the-art Recommendation Systems AI for our Ads stack. We are currently redesigning the large and fragmented Ads model space by developing new modeling architectures, advancing the scale limit through model-hardware co-design, and developing techniques that better leverage anonymized, aggregated data.

While there is significant focus on innovation, we pride ourselves in being able to translate research to production with high velocity, delivering on aggressive revenue targets in a predictable fashion. As a Data Scientist at Meta, you will collaborate on a wide array of product and business problems with a wide range of cross-functional partners across Product, Engineering, Research, Data Engineering, Marketing, Sales, Finance, and others.

You will use data and analysis to identify and solve product development's biggest challenges. You will influence product strategy and investment decisions with data, be focused on impact, and collaborate with other teams. By joining Meta, you will become part of a analytics community dedicated to skill development and career growth in analytics and beyond.

Product leadership

You will use data to shape product development, quantify new opportunities, identify upcoming challenges, and ensure that the products we build bring value to people, businesses, and Meta. You will help your partner teams prioritize what to build, set goals, and understand their product's ecosystem.

Analytics

You will guide teams using data and insights. You will focus on developing hypotheses and employ a varied toolkit of rigorous analytical approaches, different methodologies, frameworks, and technical approaches to test them.

Communication and influence

You won't simply present data, but tell data-driven stories. You will convince and influence your partners using clear insights and recommendations. You will build credibility through structure and clarity, and be a trusted strategic partner.

Responsibilities

  • Work with large and complex data sets to solve a wide array of challenging problems using different analytical and statistical approaches
  • Apply technical expertise with quantitative analysis, experimentation, data mining, and the presentation of data to develop strategies for our products that serve billions of people and hundreds of millions of businesses
  • Identify and measure success of product efforts through goal setting, forecasting, and monitoring of key product metrics to understand trends
  • Define, understand, and test opportunities and levers to improve the product, and drive roadmaps through your insights and recommendations
  • Partner with Product, Engineering, and cross-functional teams to inform, influence, support, and execute product strategy and investment decisions

Qualifications

Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience A minimum of 6 years of work experience in analytics (minimum of 4 years with a Ph.D.) Bachelor's degree in Mathematics, Statistics, a relevant technical field, or equivalent practical experience Experience with data querying languages (e.g. SQL), scripting languages (e.g. Python), and/or statistical/mathematical software (e.g.

R)

  • Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
  • Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements) Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
  • Master's or Ph.D.

Degree in a quantitative field

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