Lead Analyst, Player Analytics - jobgether

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Lead Analyst, Player Analytics based in the United States.

This role offers the opportunity to shape business strategy through advanced analytics focused on player behavior, growth, and retention. You will partner with teams across marketing, product, revenue operations, and VIP operations to uncover insights that directly influence engagement and customer value. The position combines analytical problem-solving, data storytelling, and strategic thinking in a fast-paced digital entertainment environment. You will move beyond traditional reporting by building frameworks, identifying opportunities, and delivering recommendations that drive measurable outcomes. Using modern analytics practices, machine learning concepts, and AI-powered tools, you will help elevate decision-making capabilities across the organization. This is a high-impact opportunity for an analytical leader who enjoys autonomy, collaboration, and transforming complex data into meaningful business actions.

Accountabilities:

The Lead Analyst, Player Analytics will own key analytics initiatives focused on understanding player behavior, optimizing growth strategies, and improving long-term retention. The role requires strong business partnership skills, technical expertise, and the ability to translate complex analysis into actionable recommendations.

• Partner with Revenue Operations, Marketing, Product, and VIP teams to prioritize and execute analytics initiatives focused on player acquisition, engagement, and retention.
• Lead end-to-end analytics for bonus and incentive programs, measuring performance, optimizing structures, and improving player value and return on investment.
• Analyze player behavior across multiple products to identify engagement trends, friction points, monetization opportunities, and personalization strategies.
• Develop analytical frameworks to support player segmentation, lifecycle analysis, retention strategies, and product recommendations.
• Apply machine learning concepts and AI-assisted tools to accelerate insight generation, improve workflows, and expand analytics capabilities.
• Build predictive analyses and models that support business decisions, including churn prediction, propensity scoring, and lifetime value optimization.
• Communicate findings clearly through compelling narratives, actionable recommendations, and data-driven presentations for technical and non-technical stakeholders.
• Collaborate with cross-functional teams to identify opportunities for improvement and drive measurable business impact.

Requirements: