Lead Analyst, Player Analytics
New
J
JobgetherGaming, Digital Entertainment
Based in the United StatesFull-TimeLead
Salary110,000 - 150,000 CAD per year
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Job Details
- Experience
- 3+ years
- Required Skills
- PythonSQLMachine LearningSnowflakeA/B testingdbt
Requirements
- 3+ years of experience in analytics, ideally within gaming, sports betting, or consumer products.
- Bachelor’s degree in Mathematics, Economics, Engineering, Computer Science, or a related quantitative discipline preferred.
- Strong SQL skills, including advanced concepts such as window functions, CTEs, and complex aggregations.
- Experience working with cloud data warehouses such as Snowflake.
- Strong understanding of A/B testing, experimental design, and statistical significance.
- Experience with funnel analysis, cohort analysis, player lifecycle modeling, and retention curves.
- Working knowledge of machine learning concepts and practical experience applying AI tools to analytics workflows.
- Familiarity with dbt transformation workflows, Python, segmentation models, predictive modeling, or LTV analysis is preferred.
- Ability to travel occasionally for business needs.
Responsibilities
- 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.
- 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 stakeholders.
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