Data Scientist, Analytics (Ranking, AI)

Meta; Menlo Park (CA); On-site; USD 170,000 - 250,000; Full time

Job summary

Meta seeks a Data Scientist to collaborate across Product, Engineering, Research and Analytics to solve Ads and monetization challenges. You will use data to shape product strategy, run rigorous analyses, and build models that influence investment decisions.

You will tell data-driven stories, present findings with clarity, and partner with cross-functional teams to deliver measurable impact while advancing AI capabilities and responsible practices.

Qualifications

• Bachelor's degree in CS, CE or related field.
• 6+ years analytics experience (4+ with PhD).
• Experience with SQL, Python, and R.
• Master's or PhD in a quantitative field.

Responsibilities

• Shape product development with data and insights.
• Influence strategy and investments with data-driven findings.
• Tell data-driven stories and present clear recommendations.

Skills

Data analytics

Data storytelling

Cross-functional collaboration

Education

Bachelors in CS/CE/Math

Masters/PhD in quantitative field

Tools

SQL

Python

R

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.

• 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