Data Science Manager, XR Tech End User Understanding and Growth

Key Skills

Data Engineering, Data science techniques, Machine learning techniques, Product Management

Job Description

As the Data Science Manager for XR Tech focusing on Presence Platform End User Understanding, you will lead a team of data scientists to produce insights and data assets that guide our innovation investments and go-to-market strategy, and identify ways to invent for the future of the Quest, Smart Glasses, and Family of Apps ecosystem.

XR Tech Data Science works closely with our XR Tech organization to fill Mixed Reality and the Metaverse with innovations that our users love, whether games, media, fitness, or beyond. The team is rapidly growing in a unique horizontal function at Meta.

Your primary stakeholders will be our XR Tech leadership team, including the VP of XR Tech, the VP of Content and Sales, and the senior leadership of our XR Presence, XR People, and Nimble XR Input organizations.

You will partner with product teams to help them understand how they can better incubate and mature cutting-edge features across our platforms.

You will partner closely with our Data Engineering team to scale insights and data-informed decision-making, and with other Presence Platform XFN and insights functions, including Product Management, UXR, Content, and Device Insights.

The XR Tech Data Science Manager will improve the impact of Presence Platform Features on our Quest, FoA and AR ecosystem goals by (1) directly supporting major strategic decisions and feature launches, (2) creating accountability to our product goals and insights on our performance against them, (3) identifying and driving opportunities we should resource to further the Presence Platform core technology’s on end users of Quest, AR devices, and FOA integrations like Messaging.

Key activities for this role include regular metrics reviews, mentoring and growing the team, driving goal definition and accountability, developing the analytics vision and roadmap, conducting deep dives on end user engagement/adoption, driving wide scale experimentation, and leading analytical exercises to identify new opportunities for impact or influencing major strategic decisions.

Data Science Manager, XR Tech End User Understanding and Growth 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 including 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
• A minimum of 2 years of experience working on consumer facing products
• Experience communicating both in low-level technical details as well as high-level strategies
• Experience in cross-functional partnership among teams of Engineering, Design, PM, Data Engineering
• Track-record driving product roadmap and execution

Preferred Qualifications

• Degree in Mathematics, Statistics, Computer Science, Engineering, Economics, Social Sciences or another quantitative field.
• Proven track record of leading high-performing analytics teams.
• 7+ years of experience doing quantitative analysis, statistical modeling or machine learning in the experimentation space