Data Scientist (Technical Leadership)

About the role

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• Data Scientist (Technical Leadership)
• Data Scientist (Technical Leadership)
• Sunnyvale, CA +5 locations
• Data Science
• + 1 more

Meta's products are designed to bring people closer together and make it easier for them to share information, ideas, and experiences. We are hiring experienced Data Scientist across a variety of our product areas including Facebook, Instagram, Monetization, Integrity, Gen AI, Messenger, Reality Labs, Whatsapp, and others!

As a Data Scientist, you will collaborate on a wide array of product and technical problems with a wide range of cross-functional partners across Product, Engineering, Research, Data Engineering, 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 development, quantify new opportunities, identify upcoming challenges, and ensure 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.

Data Scientist (Technical Leadership) 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
• Understand and identify opportunities to more responsibly leverage cutting edge technology
• 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
• Navigate ambiguity and complexity by forming clear hypotheses, making data-driven decisions, and proactively identifying areas for deeper investigation

Minimum Qualifications

• Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
• 8+ years of experience in data science or analytics, leading analytics work in an IC capacity, working collaboratively with Engineering and cross-functional partners, and guiding data-influenced product planning, prioritization and strategy development
• Experience working effectively with multiple stakeholders and cross-functional teams, including Engineering, PM/TPM, Analytics and Finance
• Experience framing and communication skills

Preferred Qualifications

• Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
• Masters or Ph.D. Degree in a quantitative field
• Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
• Experience with predictive modeling, machine learning, and experimentation/causal inference methods
• Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)

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