Insights Analyst, Dispute Experience

Analytics & Root-Cause Identification

• Translate complex data signals into root-cause narratives and actionable operational or product recommendations.
• Define success metrics, KPIs, monitoring frameworks, and develop predictive analytics mechanisms for Disputes and Fraud Ops.
• Build enhanced metric and KPI frameworks that help identify emerging trends, performance shifts, and anomalies across Fraud Ops and Dispute Ops.
• Conduct in-depth workflow and lifecycle analyses—such as examining interaction patterns, segment behaviors, and before/after performance changes—to identify friction points, root causes, and opportunities to improve Trust & Safety processes.

AI-Enabled Automation & Decisioning

• Partner with Product, Engineering, Fraud Strategy, and Ops to evaluate and improve AI-enabled workflows, including automated decisioning, classification models, and agent augmentation tools.
• Analyze model outputs, false positives/negatives, and decision accuracy to recommend enhancements.
• Measure operational and member impact of automation changes and support the design of new AI features (e.g., routing logic, contact summarization, anomaly detection).

Experimentation & Evaluation

• Design and evaluate A/B tests, policy changes, rule deployments, workflow adjustments, and operational interventions.
• Surface statistically robust insights to inform go/no-go decisions and rollout strategies.
• Develop sizing models that quantify the operational, financial, and member impact of proposed policy, workflow, or automation changes, providing clear forecasts that support data-driven rollout decisions.

Executive Communication & Storytelling

• Create concise, executive-level summaries that distill investigations into clear narratives with quantified impacts and recommended next steps.
• Present Trust & Safety insights to senior leadership across Operations, Risk, Product, and Engineering.

Cross-Functional Alignment & Leadership

• Work closely with Operations, Risk, Product, and Engineering to ensure shared understanding of problems and alignment on solutions.
• Proactively communicate progress, updates, timelines, and risks across stakeholders.
• Help drive clarity in ambiguous environments, especially during live issues and emerging operational events.

Technical Skills

• Bachelor’s degree in a quantitative field (Statistics, Data Science, Economics, Mathematics, Computer Science, Engineering).
• 5+ years in analytics, ideally in Trust & Safety, Fraud, Risk, or Support Operations.
• Advanced SQL skills (complex joins, window functions, CTEs).
• Proficiency in Python or R for experimentation, modeling, and data analysis.
• Strong understanding of statistics, causal inference, experiment design, and anomaly detection.
• Experience analyzing automation systems, risk models, or ML-driven workflows is a plus.
• Experience with BI tools such as Looker or Mode.

Domain Expertise

• Experience in fintech, digital banking, payments, fraud, or Trust & Safety environments.
• Experience evaluating or supporting AI/ML systems, including monitoring false positives/negatives and model performance.
• Ability to break down ambiguous issues into structured analytical frameworks.
• Strong understanding of how product, operations, and engineering intersect in Trust & Safety domains.

Communication & Leadership

• Outstanding written and verbal communication skills, especially in synthesizing complex topics for executives.
• Proven track record of taking end-to-end ownership of cross-functional initiatives.
• Ability to drive alignment and decision-making across teams.
• Mindset of strong ownership and urgency, proactive communication, and a bias toward action and clarity.