Data Analyst

GitHub Copilot is changing how the world builds software—and protecting that experience from abuse is critical to our users, our customers, and the integrity of the platform. We're looking for a data analyst to focus on identifying, investigating, and reporting on abuse, misuse, and policy violations of GitHub Copilot and other AI-powered developer products.  

In this role, you'll turn Copilot telemetry, account signals, and usage data into clear insights that inform detection, policy, and enforcement decisions. You'll partner closely with Trust & Safety, Product, Engineering, Security, and Legal (CELA) to surface emerging abuse trends and help shape how we respond to them. 

• Detect and investigate Copilot abuse. Analyze telemetry, usage logs, billing/entitlement data, and account signals to identify abuse patterns such as automated or scripted usage, account and credential sharing, token misuse, evasion of usage limits, prompt injection, content-policy violations, and fraudulent or coordinated activity. 

• Build the analyses and tooling that power Trust & Safety decisions. Develop dashboards, reports, and prototype models (e.g., abuse-risk scoring, anomaly detection, investigation views) that help stakeholders distinguish legitimate Copilot usage from abuse and measure the impact of mitigations. 

• Tell the story behind the data. Synthesize complex analyses into clear, actionable insights—through dashboards, written reports, and stakeholder presentations—that influence detection rules, policy updates, and enforcement actions. 

• Improve data quality and coverage. Identify gaps in abuse-relevant data sources, recommend new pipelines or integrations, and partner with Engineering and Data Science to strengthen the signals available for Copilot abuse detection. 

• Anticipate analytical tradeoffs. Evaluate methods and assumptions critically, surface risks like false positives/negatives and unintended impact on legitimate users, and recommend mitigations. 

• Operationalize what works. Turn one-off investigations into reusable, self-service reporting where it makes sense, and help scale recurring abuse-monitoring capabilities. 

• Collaborate across the company. Work with Trust & Safety, Copilot Product, Anti-Abuse Engineering, Security, Data Science, and CELA to align on data definitions, abuse taxonomies, and shared standards. 

• Handle sensitive data responsibly. Apply data privacy, governance, and ethical handling practices to Copilot prompt, completion, and user-activity data, and ensure work goes through appropriate CELA review. 

• Mentor and influence. Share domain expertise with peers, coach less experienced analysts, and present findings to senior Copilot, Trust & Safety, and Security leadership. 

Required Qualifications   

• 5+ years experience in data analysis and reporting, data science, business intelligence, or business and financial analysis o OR Bachelor's Degree in Statistics, Mathematics, Analytics, Data Science, Engineering, Computer Science, Business, Economics or related field AND 3+ years experience in data analysis and reporting, data science, business intelligence, or business and financial analysis
  • o OR equivalent experience.

Preferred Qualifications  

• 7+ years of relevant experience, or a Bachelor's/Master's degree in a related field with 5+/3+ years of experience respectively 

• 1+ year of experience with SQL, Python, Power BI, or comparable data analytics tools  

• 2+ years of consulting experience  

• Experience analyzing abuse, fraud, or Trust & Safety signals on a large-scale online platform—ideally involving AI/ML products such as GitHub Copilot or similar AI developer tools  

• Familiarity with common abuse patterns on developer platforms (automated/scripted usage, account and credential sharing, evasion of usage limits, prompt injection, content-policy violations)  

• Experience partnering with Trust & Safety, Anti-Abuse, Security, and Legal teams on sensitive investigations  

Skills You'll Use  

• Analytics & Storytelling: Data analysis, data modeling, data visualization, data storytelling, statistics, quality reviews 

• Trust & Safety: Abuse and anomaly detection, behavioral analytics and pattern recognition, investigative analysis, Trust & Safety analytics 

• Technical: SQL, Python, big data, database querying, automation, data integration, data cleaning 

• Collaboration & Influence: Technical communication, consulting, influencing without authority, cross-functional partnership 

• Judgment: Decision-making, problem solving, operational excellence, compliance and data governance