Staff Data Analyst - Lifetime

Fanatics•New York, NY

18h•$160,000 - $190,000

About The Position

As Fanatics Betting & Gaming scales, accurately understanding and forecasting customer lifetime value (LTV) is foundational to how we deploy capital, evaluate acquisition efficiency, and drive long-term profitability. As the Staff Data Analyst – Lifetime Value, you will serve as the business owner of the LTV framework. You will be responsible for the integrity, evolution, and business adoption of our LTV model — ensuring it is methodologically sound, operationally scalable, and deeply embedded in decision-making across Marketing, Finance, Product, and executive leadership. This role sits at the intersection of advanced analytics and strategy. You will partner closely with Data Science on model development and validation, while translating complex modeling outputs into clear, actionable insights that drive acquisition strategy, forecasting, and long-term value creation. You will live and breathe LTV — owning not just reporting, but how the organization understands, trusts, and acts on customer value. The ideal candidate combines strong statistical fluency with executive-level communication skills, thrives in high-growth environments, and is comfortable taking ownership of a core enterprise metric.

Requirements

7 plus years of experience in advanced analytics, data science, or strategic analytics roles, with direct experience owning or contributing to predictive models.
Experience partnering with Data Science teams to productionize and monitor predictive models, with experience with Lifetime Value models a strong advantage.
Experience building executive-level reporting and translating complex model outputs into clear business insights.
Strong understanding of statistical modeling techniques (e.g., survival analysis, regression, time-series forecasting, multivariate testing), with hands-on experience in Python, R, or similar languages.
Advanced SQL proficiency with experience building scalable datasets and supporting model inputs.
Strong understanding of acquisition economics, including CAC, payback periods, contribution margin, and ROI frameworks.
Experience integrating model outputs into financial planning, forecasting, or capital allocation decisions.
Proficiency in data visualization tools (Tableau, Power BI, or similar) with a focus on executive communication.
Demonstrated ability to influence senior leadership and drive adoption of analytics-based decision frameworks.
Proven ability to manage multiple high-impact initiatives in a fast-paced, high-growth environment.
A passion for working in an entrepreneurial, fast-paced environment with a focus on agility and rapid decision-making.
Willingness to travel as needed to attend key meetings across Fanatics offices.

Nice To Haves

Experience in sports betting or iGaming is a strong advantage.

Responsibilities

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.