Data Science Manager, Serving Infra
Meta

Job Info

As a Data Science Manager at Meta, you will help shape the future of the experiences we build for billions of people and hundreds of millions of businesses, creators, and partners around the world.We are looking for a Data Science Manager who is passionate about analytics for large scale ML systems to help drive data-informed decisions for the Monetization Infrastructure team and help shape the long term strategic direction of the development of this system.This Data Science Manager will partner with Engineering and Product leads across Meta Ads and AI infrastructure, to guide the strategic direction for one of the largest-scale ML infrastructure systems in the world, developing new ML infrastructure capabilities. The ideal candidate has experience with large-scale systems and a data product background.About the role:Product leadership: You will use data to understand the product and business ecosystem, quantify new opportunities, identify upcoming challenges, and shape product development to bring value to people, businesses, and Meta. You will help develop strategy and support leadership in prioritizing what to build and setting goals for execution.Analytics: You will guide product teams using data and insights. You will focus on developing hypotheses and employ a diverse 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 leaders using clear insights and recommendations. You will build credibility through structure and clarity, and be a trusted strategic partner.People leadership: You will inspire, lead and grow a world-class team of data scientists and data science leaders.

Data Science Manager, Serving Infra Responsibilities:

• Drive analytics projects end-to-end in partnership with teams from Engineering, Product Management and across the Analytics community to inform, influence, support, and execute product strategy and investment decisions.
• Inspire, lead, and grow a team of data scientists and manager(s) to fulfill our longer term vision.
• Actively influences the design of the strategy and shaping of the roadmap within this scope. Generate and use team insights to set and prioritize longer term goals.
• Develop understanding of complex distributed systems and sub-components, as well as broader industry challenges, to identify present and future risks and opportunities.
• Work with large and complex data sets to solve a wide array of challenging problems using different analytical and statistical approaches.
• Grow analytics expertise around you, upskilling your team, engineers, and others, to increase overall team impact.

Minimum Qualifications:

• BS degree in a quantitative discipline (e.g., statistics, operations research, econometrics, computer science, engineering), or BS/MS in a quantitative discipline with equivalent working experience.
• A minimum of 7 years of work experience (3+ years with a Ph.D.) in applied quantitative field, including a minimum of 2 years of experience managing analytics teams.
• 5+ years of experience in a team leadership role, including 2+ years of experience with people management through layers.
• Experience communicating in both low-level technical details, as well as high-level strategies.
• Experience in cross-functional partnership among teams of Engineering, Product Management, and Data Engineering.
• Track-record driving product roadmap and execution.

Preferred Qualifications:

• Master's or Ph.D. degree in Mathematics, Statistics, Computer Science, Engineering, Economics, or another quantitative field.
• 7+ years of experience doing quantitative analysis, statistical modeling or machine learning in the experimentation space, including 3+ years of experience in the gaming or media industry.
• Experience partnering with and influencing stakeholders of all layers and functions, from Product to Engineering to Finance.
• Experience working in infrastructure, financial products, or supply chain management.
• Knowledge of system optimization techniques.

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