Business Analyst II

Operates effectively across complex cross-functional environments, balancing customer, business, operational, policy, and technical considerations. Partner with Policy, Engineering, Data Science, and Operations teams to develop scalable policy solutions that improve ecosystem safety, trust, and quality. Define and evolve policy frameworks, reviewer guidelines, decision criteria, and operational playbooks to drive consistency and enforcement quality. Translate policy requirements into operational processes, product requirements, evaluation frameworks, and scalable enforcement mechanisms. Drive policy-to-model enablement by partnering with engineering and data science teams to improve AI- and ML-powered review, detection, and enforcement systems. Generate insights from labeling, evaluation, audit, and operational data to identify quality gaps, emerging risks, policy ambiguities, and opportunities for improvement. Perform hands-on labelling, content review, evaluation, and adjudication activities as needed to establish ground truth, validate evaluation methodologies, calibrate quality standards, and improve model performance. Define quality metrics, evaluation methodologies, and feedback loops to continuously improve human review and automated decision-making systems. Partner with vendor and review operations teams to improve annotation quality, reviewer effectiveness, operational efficiency, and policy comprehension. Drive cross-functional initiatives from problem definition through execution, balancing customer experience, operational needs, policy intent, and business objectives. Communicate insights, recommendations, and policy decisions effectively to technical and non-technical stakeholders. Bachelor's degree in business, Public Policy, Communications, Operations, Economics, Psychology, Social Sciences, Data Science, Analytics, Computer Science, or a related field, or equivalent practical experience. 3+ years of experience in Product Management, Program Management, Trust & Safety, Policy Operations, Risk Management, Fraud Prevention, Content Moderation, Marketplace Integrity, or related domains. Experience developing, interpreting, operationalizing, or enforcing policies, guidelines, standards, or business rules in complex operational environments. Strong analytical and problem-solving skills, with experience leveraging data to drive decisions, identify opportunities, and improve quality and operational outcomes. Experience working across cross-functional teams, including Engineering, Data Science, Operations, Policy, and Business stakeholders. Experience designing or managing review workflows, quality programs, labeling operations, evaluation frameworks, or operational processes at scale. Strong communication, stakeholder management, and influencing skills, with the ability to drive alignment across diverse teams. Familiarity with AI/ML-powered systems and the role of human review, labeling, evaluations, and feedback loops in improving model quality and safety outcomes. Experience working in large-scale Trust & Safety, Marketplace Integrity, Fraud Prevention, Online Safety, Risk Management, Compliance, or similar ecosystems. Experience designing and scaling human review, labeling, policy enforcement, quality assurance, or evaluation programs that support operational and product outcomes. Experience supporting AI/ML model development through labeling strategies, ground-truth creation, adjudication frameworks, evaluation design, and feedback loops. Familiarity with Generative AI, Agentic AI, Large Language Models (LLMs), and human-in-the-loop systems used to improve safety, quality, and operational effectiveness. Experience generating actionable insights from large-scale operational, quality, labeling, or enforcement datasets and translating findings into policy, process, tooling, or product improvements. Working knowledge of SQL, Power BI, Python, or similar analytics, reporting, and experimentation tools. Demonstrated success leading cross-functional initiatives involving Engineering, Data Science, Operations, Policy, and Business stakeholders. Proven ability to thrive in fast-paced, ambiguous environments while driving measurable customer and business impact through ownership, customer focus, and data-driven decision making.