Note: The job is a remote job and is open to candidates in USA. Meta builds technologies that help people connect, find communities, and grow businesses. Meta is seeking a seasoned Data Science Director to lead distributed data science teams, shape product and business strategy through rigorous analytics, and drive data-informed decisions across the organization.
Responsibilities
- Inspire, lead and grow a distributed team of data scientists and data science leaders across North America
- Apply your expertise in quantitative analysis, data mining, and the presentation of data in developing data-informed strategies for growing and improving our product offerings
- Contribute to long-term technical vision and strategy methods and metrics that will improve the quality and efficiency of our products at scale
- Lead development and communication of product area insights and recommended action items with Meta Analytics leadership
Skills
- 10+ years of work experience managing analytics teams, working collaboratively with Product and Infrastructure teams, and guiding data-influenced product and business planning, prioritization and strategy development
- Demonstrated experience in hiring, retaining and scaling geographically dispersed, high-performing teams with a broad range of experiences, perspectives, approaches, and backgrounds
- Proven experience influencing strategy and driving change across org boundaries through clear and compelling communication of data-driven insights and analyses
- Intellectual curiosity, drive, and decisiveness with ambiguous and complex enviornments
- Experience working effectively with multiple stakeholders and cross-functionally
- Effective communication skills
- Experience with both product and business analytics work and demonstrated to deliver an integrated approach
- 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)
- Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
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