Data Scientist III

About the Role

Key Responsibilities

• Design and lead analyses to understand key customer behaviors (e.g., retention, early value, churn risk, lifecycle segments).
• Develop frameworks to measure the effectiveness of customer engagement strategies and loyalty initiatives.
• Translate complex findings into compelling narratives and clear recommendations for executive stakeholders.
• Build and own production-grade models (e.g., LTV prediction, churn modeling, segmentation).
• Design A/B and multivariate experiments to test hypotheses around personalized experiences, early win strategies, and lifecycle interventions.
• Partner with engineering to productionize and monitor model performance.
• Act as a thought partner to Product, Marketing, and Lifecycle teams on targeting, personalization, and measurement.
• Partner with the Fan Ecosystem and FBG Data Engineering teams to define scalable data assets that support customer analytics and experimentation.
• Present insights and strategic recommendations directly to senior leadership.
• Mentor junior analysts and data scientists on statistical methods, modeling techniques, and analytical approaches.
• Champion analytical rigor, data quality, and ethical use of customer data across the team.

Requirements

• 5+ years of experience in data science or analytics, preferably in a consumer-facing digital business (e.g., eCommerce, gaming, fintech, or mobile apps).
• Strong proficiency in SQL and Python; experience with large-scale distributed data (e.g., Spark).
• Deep experience designing and analyzing experiments; comfort with causal inference techniques is a plus.
• Proven track record of developing and deploying predictive models in production.
• Strong business acumen and the ability to influence cross-functional stakeholders at all levels.
• Experience with customer lifecycle analysis, segmentation, or personalization is highly desirable.
• Bachelor's degree in a quantitative field (e.g., Statistics, Economics, Computer Science); advanced degree a plus.

Nice to Have

• Familiarity with sports betting, iGaming, or loyalty programs.
• Experience with Databricks, Airflow, or MLOps tools.

Qualifications

• Bachelor's degree in a quantitative field (e.g., Statistics, Economics, Computer Science).
• Advanced degree is a plus.

Benefits & Perks

• Salary range of $128,000 - $180,000 per year, based on experience and location.
• Eligible for annual bonus and equity award.
• Full-time employees are eligible for Medical, Dental, Vision, 401K, paid time off, and other benefits like GymPass, Pet Insurance, Family Care Benefits, and more.
• $700 home office setup stipend.
• Remote position with residency requirements in specific states or hybrid role based in Denver, CO.