Data Science Director (IC)

Meta

$180K — $220K *
Consumer Technology
8 - 10 years of experience
Job Overview by Ladders

Qualifications

  • Bachelor's degree in a technical field or equivalent experience
  • 10+ years in analytics and data science, with leadership in IC roles
  • Collaborative experience with Engineering and cross-functional teams
  • Strong framing and communication skills
  • Demonstrated ability to influence stakeholders effectively

Responsibilities

  • Collaborate with cross-functional teams to inform and execute Ads AI strategy
  • Contribute to long-term vision for analytics methods in Ads AI
  • Enhance AI development through experimentation and measurement improvements
  • Understand large-scale AI systems to identify risks and opportunities
  • Inspire and lead a team of data scientists across multiple teams

Benefits

  • Opportunity to work with cutting-edge AI technologies
  • Collaborative environment with Product and Engineering teams
  • Influence strategic decisions impacting a major revenue stream
  • Engage in ongoing professional development in AI
  • Contribute to ethical AI practices and responsible technology use
Full Job Description
We are seeking a Data Science Director IC who is passionate about developing, measuring, and strategizing investments in the Monetization Ranking AI space. Our Monetization AI system utilizes state-of-the-art Machine Learning/AI to ensure every ad impression is meaningful. It is central to the entire ads experience we offer to users and advertisers and generates the vast majority of Meta's revenue. The mission of this role is to drive data-informed decisions and shape the long-term strategic direction and operational model of the Monetization AI Ranking space in an IC capacity. The Data Science Director will collaborate with Engineering and Product executives across Meta Ads and the Family of Apps Monetization teams. The candidate will have extensive experience in large-scale AI development, measurement, and data product management, and will be enthusiastic about cutting-edge AI advancements. They should have a proven ability to influence executives through their data, framing, technical, and communication skills.

Responsibilities

Collaborate with Engineering, Product, and cross-functional teams to inform, influence, support, and execute strategy and investment decisions for the Ads AI space
• Contribute to the long-term technical vision and strategy for analytics methods and metrics to enhance the quality and efficiency of Ads AI at scale
• Work with engineering and other data scientists to build and improve AI development, automation, experimentation, and measurement methods and metrics, ensuring high-quality throughput and impact
• Develop an understanding of complex, large-scale AI development, experimentation, and measurement systems, as well as broader industry challenges, to identify current and future risks and opportunities
• Inspire and lead a team of data scientists and managers across multiple teams in close collaboration with other Data Science Directors and Managers

Minimum Qualifications
• Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
• 10+ years of experience in analytics and data science, leading analytics work in 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
• 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)
• Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies

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