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Data Analyst, Global Operations

Meta

$150,000 - $213,000
Nov 1, 2025
New York, NY, United States of America
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At Meta, the business problem is to drive impact through data & analytics by combining analytical expertise with an AI-first mindset to design and deploy analytical solutions that drive strategic decision-making and operational efficiency at a global scale.

Requirements

  • Advanced proficiency in SQL, and experience with Python, for data analysis and automation
  • Proven experience building and deploying scalable data pipelines and productionizing dashboards using BI tools (e.g., Tableau)
  • Track record of proactively using AI tools to drive productivity and innovation across projects and teams
  • Solid understanding of statistical analysis with practical application to real-world business problems
  • Hands-on experience with AI/ML model development, deployment, and/or integration

Responsibilities

  • Partner with operations, data science, engineering, and product teams to address complex business challenges through AI-first analytical strategies and solutions
  • Build, maintain, and optimize scalable data pipelines, dashboards, automated reports, and self-service tools for teams across Meta
  • Champion the adoption and integration of AI tooling within analytics workflows, educating and uplifting team proficiency in modern solutions and innovative thinking
  • Communicate complex insights clearly to technical and non-technical stakeholders, influencing decisions and business outcomes
  • Continuously explore and experiment with emerging technologies and automation methods to advance the team’s analytics offerings

Other

  • 7+ years of experience in analytics, operations, product, engineering, or related fields—ideally within deadline focused tech environments or consulting
  • Bachelor’s degree in a quantitative discipline such as Computer Science, Data Science, Statistics, Mathematics, Operations Research, or equivalent practical experience
  • Master’s degree or higher in a quantitative discipline such as Data Science, Statistics, Mathematics, Computer Science, or a related field
  • Demonstrated ability to independently manage analytics projects end-to-end, from problem definition to business adoption and impact measurement