Lead QA Analyst - Trust and Safety

Company Description LinkedIn is the world’s largest professional network, built to create economic opportunity for every member of the global workforce. Our products help people make powerful connections, discover exciting opportunities, build necessary skills, and gain valuable insights every day. We’re also committed to providing transformational opportunities for our own employees by investing in their growth. We aspire to create a culture that’s built on trust, care, inclusion, and fun – where everyone can succeed. Join us to transform the way the world works. Job Description At LinkedIn, our approach to flexible work is centered on trust and optimized for culture, connection, clarity, and the evolving needs of our business. The work location of this role is hybrid, meaning it will be performed both from home and from a LinkedIn office on select days, as determined by the business needs of the team.

Join LinkedIn’s Trust & Safety organization as a Lead QA Analyst, where you will play a critical role in driving decision quality, policy adherence, and enforcement consistency across Ads review workflows. This role sits within Trust Review Operations and is central to detecting Digital marketing Abuses and enforcement decisions that align with platform policies, regulatory requirements, and business standards.As a Lead QA, you will not only own quality outcomes but also shape the evolution of QA frameworks, help customise AI model and check precisions , support in enhancing tooling requirements and data-driven insights. Over time, this role may expand to support other moderation workflows, requiring adaptability across multiple lines of business. You will act as a key connector across Policy, Product, Engineering, and Data Science, influencing both operational excellence and long-term strategy to ensure LinkedIn remains a safe, trusted, and compliant platform globally.

Responsibilities: Own end-to-end QA strategy and execution for Ads workflows, including areas such as abuse, fraud, scams, and policy violations Design, develop, and maintain golden datasets and benchmarking frameworks to measure and improve decision quality across multiple programs Own quality outcomes , shape the evolution of QA frameworks, help customise AI model and check precisions , support in enhancing tooling requirements and data-driven insights Analyze error trends and conduct deep-dive root cause analyses (RCA) to identify systemic gaps and recommend scalable solutions Drive improvements across VAs, Prompt engineering, Policy interpretation, and Operational workflows Identify and implement process improvements, Automation Opportunities, and tooling enhancements to increase efficiency, accuracy, and scalability Lead calibration forums and coaching sessions to ensure consistency in policy enforcement and reviewer decision-making both for internal & external vendors Partner closely with Trust Product, Policy Operations, Engineering, and Data Science to influence moderation tooling and quality frameworks Document insights, trends, and quality metrics, and support internal and external audit/compliance requirements Influence stakeholders and drive alignment on quality goals, performance standards, and continuous improvement initiatives Conduct second-level quality reviews of QA outputs to ensure high standards of accuracy, consistency, and reliability Collaborate with Policy and Training teams to drive clarity, reduce ambiguity, and ensure consistent policy application Act as a bridge between Quality Managers, QA teams, and cross-functional stakeholders, enabling seamless communication and execution Qualifications Basic Qualifications Bachelor’s degree in any field. 7+ years of experience in Trust & Safety, decision quality assurance, or digital marketing policy domains. Experience conducting error trend analysis and root cause analysis in moderation/QA environments. Demonstrated experience designing and implementing decision quality measurement frameworks or solutions. Hands-on experience with large language models, including prompt engineering, iteration, debugging, and deployment. Experience applying regulatory compliance and policy enforcement frameworks in a content moderation or ads review context. Experience performing data analysis to drive operational improvements (e.g., using SQL or Python to analyze QA outcomes). Preferred Qualifications Familiarity with MDSS, DSA, and global content policy frameworks Knowledge of SQL, Python, PowerBI, Tableau, etc Experience with Copilot Studio, QA automation, and Error analysis Expertise in Virtual agent / Prompt engineering, Prompt iteration, debugging and deployment. Expert in understanding, defining and implementing Decision Quality Measurement frameworks / solutions Leadership in cross-functional environments Passion for quality and continuous improvement Suggested Skills Technical Proficiency (QA tools, LLM moderation, prompt engineering, data science) Policy & Regulatory Knowle