• Partner with Data Science to develop and enhance predictive LTV models, contributing to feature design, cohort logic, retention modeling, revenue forecasting, and profitability estimation. • Act as the cross-functional authority on customer value, aligning Sportsbook, Casino, Marketing, Finance, Product, and Operations on interpretation and usage of LTV metrics. • Lead LTV-based acquisition performance measurement, shifting focus from short-term KPIs to long-term value creation (LTV/CAC, payback periods, cohort quality). • Translate model outputs into business narratives, enabling executives and senior stakeholders to understand drivers of value, forecast changes, and strategic trade-offs. • Standardize LTV reporting across the organization, ensuring consistent definitions, transparency of assumptions, and alignment between Analytics, Finance, and Marketing. • Embed LTV into strategic planning and capital allocation decisions, partnering with Marketing and Finance stakeholders on forecasting, scenario modeling, and long-range planning. • Drive scalable executive reporting, developing dashboards and materials that clearly communicate cohort trends, forecast updates, risks, and opportunities. • Strengthen the data foundation supporting LTV, partnering with Data Engineering to improve data pipelines, documentation, and quality controls.

• Trace issues back through data pipelines or code to uncover why something isn’t behaving as expected. • Write clear, concise Jira tickets explaining what was found, how it was fixed, and how to prevent similar issues. • Partner with engineers and product managers to make data and code more reliable, faster, and easier to understand. • Help build a culture of technical curiosity — where asking why and testing assumptions is part of every release. • Leverage AI tools to think, build, and ship faster.

• Build dashboards and technical reports to monitor portfolio health, strategy performance, and risk indicators; • Conduct detailed analyses of the credit portfolio, identifying emerging risks, trends, and opportunities for improvement; • Prepare and present reports and executive materials on credit origination, delinquency, and the effectiveness of credit strategies; • Turn large volumes of data into actionable insights to support strategic decisions; • Develop analyses to support the implementation and modification of mass-market credit policies; • Contribute to the continuous improvement of models, scorecards, and decision rules through analysis and monitoring; • Collaborate with product, commercial, and compliance teams to ensure alignment between strategy and operations; • Support the collections team with data, metrics, and analyses that guide improvements to the collections process and effective recovery of delinquent customers.

• Help define and document our event taxonomy, and work directly with product teams to define new events that are consistent with our overarching event structure and taxonomy • Dive into our data to discover insights on player behavior across different cohorts, segments, and stages of the user lifecycle, and communicate/collect feedback on your findings from peers, business, and product partners • Work with business, product, and engineering teams to [plan → design → ship → evaluate] experiments/AB-Tests, and communicate their results/impact with the company. • Work directly in Amplitude and BigQuery to extract truths and trends that inform and guide business and product decisions • Develop tools and best practices to ensure high data quality and identify and prevent errors before they reach production datasets • Collaborate with analysts/data-scientists in building scalable and reproducible analyses, and communicate their insights with the company