Data Scientist - Law Enforcement Analytics & Program

About Meta

Meta builds technologies that help people connect, find communities, and grow businesses. When Facebook launched in 2004, it changed the way people connect. Apps like Messenger, Instagram and WhatsApp further empowered billions around the world. Now, Meta is moving beyond 2D screens toward immersive experiences like augmented and virtual reality to help build the next evolution in social technology. People who choose to build their careers by building with us at Meta help shape a future that will take us beyond what digital connection makes possible today—beyond the constraints of screens, the limits of distance, and even the rules of physics.

Meta is proud to be an Equal Employment Opportunity and Affirmative Action employer. We do not discriminate based upon race, religion, color, national origin, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, transgender status, sexual stereotypes, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics.

Role Overview

Meta is seeking a Data Scientist to join our Law Enforcement Analytics & Program (LEAP) team. Our mission is to enable Meta's capacity to balance public safety, user privacy, and compliance obligations at scale through strategic coordination, technical leadership, and operational success across the Security, Integrity, Investigations (SI2) legal team and its partners. This role leverages AI, data and insights to drive decisions that enable predictability, and proactive detection, allowing us to operate efficiently and reliably at scale. You will be building AI models and will be involved in the design, development, and deployment of intelligent solutions that reduce the operational and investigative burden. This role will directly impact the scalability and efficiency of our operations and investigations through proactive detection, automation, and resolution of routine tasks, inefficiencies, and incidents. In addition, this is a crucial role in translating data into action and identifying opportunities for efficiency and effectiveness.

Responsibilities

• Translate business challenges into clear, actionable requirements for AI-enabled solutions
• Map end-to-end business processes, highlighting areas where AI can drive efficiency and value
• Design, build and implement AI automations
• Develop and deploy solutions and AI prompts to identify and address bottlenecks, replacing manual interventions with intelligent automation
• Create scalable automation mechanisms that proactively monitor, analyze, and report
• Build robust predictive models using statistical and machine learning techniques to forecast risks, anticipate issues and optimize
• Develop monitoring tools to trigger early warnings and facilitate rapid resolution through automation
• Acts as a data subject matter expert
• Formulate the right metrics, measures, and definitions of success to drive quality, efficiency, cost, and timeliness understanding the source data
• Perform complex data analysis leveraging data streams available to drive proactive and predictive action
• Partner with operational analysts, investigators and engineering partners to understand pain points, identify repetitive tasks and translate them into opportunities for automation
• Bring multiple areas of business and engineering together via a common reliable foundation of common data, metrics, and insights
• Build data tables and dashboards, and leverage these for interpreting incidents and trends
• Leverage tools like Tableau, Python, and SQL to drive efficient analytics

Minimum Qualifications

• Bachelor's Degree in an analytical field (e.g., Computer Science, Engineering, Mathematics, Statistics, or Data Science)
• 6+ years of experience in analytics, engineering, and use of AI/ML
• 6+ years of SQL development experience and scripting language like Python
• Hands‑on experience with AI/ML frameworks (e.g., TensorFlow, PyTorch, Scikit‑learn) and automation tools/platforms
• Significant experience with data visualization tools and leveraging data models to drive business decisions
• Experience with statistics (e.g., statistics basics, statistical modeling, experimental design, hypothesis testing, etc.)
• Demonstrated experience with proactively identifying, scoping and implementing solutions
• Hands‑on experience analyzing and interpreting data, drawing conclusions, defining recommended actions, and reporting results across stakeholders

Preferred Qualifications

• Master's Degree in an analytical field (e.g., Computer Science, Engineering, Mathematics, Statistics, or Data Science)