Senior Director Applied Data Science

Sphere Entertainment Co. is a leader in immersive experiences, technology and media. The Company includes Sphere, an experiential medium powered by advanced technologies. The first Sphere opened in Las Vegas, with a second venue planned for Abu Dhabi. In addition, the Company includes MSG Networks, which operates two regional sports and entertainment networks, MSG Network and MSG Sportsnet, as well as a direct-to-consumer and authenticated streaming product, MSG+, delivering a wide range of live sports content and other programming. More information is available at

Who are we hiring?

Sphere Entertainment is seeking a Senior Director, Applied Data Science - Revenue & Optimization to lead the data science capabilities that support revenue growth across Sphere Experience ticketing and hospitality products.

This role will apply advanced statistics, probabilistic thinking, economic theory, forecasting, and optimization to solve complex revenue-management problems and inform strategic decisions across Sphere Experiences.

The Senior Director will own the methodology and strategic direction for demand forecasting, pricing, yield, and revenue optimization, while partnering closely with Revenue Strategy, Data & Analytics, Sales & Marketing, Ticketing/Box Office, Finance, and other stakeholders.

Reporting to the VP of Revenue Strategy, the Senior Director will serve as a senior quantitative thought partner and will be responsible for ensuring that data science models are not only accurate, but also interpretable, actionable, and aligned with business objectives.

A critical expectation of this role is the ability to explain why models are producing their projections, including the underlying demand drivers, assumptions, sensitivities, and sources of uncertainty, and translate those findings into clear recommendations for senior leadership.

The Senior Director will also establish and lead a scalable applied data science capability as Sphere Experiences continue to evolve and expand.

What will you do?

Lead Applied Data Science Strategy

• Own the data science strategy supporting revenue growth and optimization across Sphere Experiences.
• Establish and evolve the methodologies used for demand forecasting, pricing, yield management, inventory optimization, and revenue modeling.
• Define standards for model development, validation, evaluation, monitoring, documentation, and continuous improvement.
• Identify opportunities where advanced analytics and data science can materially improve revenue performance and business decision-making.
• Develop scalable methodologies that can be applied across Sphere Experiences, products, venues, and markets.
• Partner directly with the VP of Revenue Strategy to translate strategic revenue objectives into analytical and modeling priorities.

Own Demand Forecasting

• Own the methodology and framework for Sphere Experience demand forecasting.
• Lead the development, deployment, evaluation, and continuous improvement of occupancy, ticket sales, revenue, and demand forecasting models.
• Incorporate booking pace, historical demand, pricing, inventory, seasonality, customer behavior, marketing activity, day-of-week, segmentation, and other relevant demand drivers.
• Develop forecasting approaches for new experiences and products where historical data may be limited.
• Establish appropriate measures of forecast accuracy and model performance.
• Identify and explain the underlying drivers of changes in forecasts and demand projections.
• Develop scenario-based forecasts to support strategic pricing, inventory, and revenue decisions.

Lead Pricing & Yield Optimization Frameworks

• Own the development and evolution of pricing and yield optimization models.
• Partner with Revenue Strategy to translate forecasts and model outputs into pricing and inventory recommendations.
• Develop models to evaluate price elasticity, willingness to pay, demand curves, and customer response to pricing changes.
• Evaluate optimal inventory allocation and release strategies across price levels, customer segments, channels, and booking windows.
• Perform financial modeling of recommended pricing, discount, and inventory strategies to evaluate expected revenue impact.
• Develop scenario and sensitivity analyses to assess potential outcomes under alternative pricing and inventory strategies.
• Evaluate cross-price relationships between tickets and other revenue-generating outlets, including F&B, merchandise, parking, hospitality, and other products.

Explain Model Performance and Drivers

• Establish a high standard for analytical transparency and model explainability.
• Clearly articulate why forecasts and model projections change over time.
• Identify the underlying factors driving changes in demand, occupancy, revenue, and other key performance indicators.
• Distinguish meaningful changes in underlying demand from statistical variation or model behavior.
• Assess model assumptions, limitations, sensitivities, and uncertainty.
• Diagnose unexpected model results and determine whether they reflect changes in the market, customer behavior, data, or model methodology.
• Clearly communicate the implications of model outputs to Revenue Strategy and senior leadership.
• Provide independent quantitative judgment and challenge assumptions when the data does not support a proposed strategy.

Measure Commercial and Marketing Impact

• Partner with Marketing to quantify the impact of campaigns, promotions, and other initiatives on future demand and revenue.
• Develop analytical frameworks to measure incremental demand and revenue impact.
• Apply experimentation, statistical inference, and causal analysis where appropriate.
• Evaluate customer and market behavior to identify opportunities for revenue growth.
• Develop analytical approaches to understand relationships between ticketing, hospitality, and other Sphere revenue streams.

Build Revenue Decision Science

• Develop tools and frameworks that enable Revenue Strategy leaders to evaluate "what-if" scenarios and alternative strategies.
• Translate predictive models into actionable business recommendations.
• Quantify expected financial outcomes associated with different revenue strategies.
• Develop analytical frameworks that connect demand, pricing, inventory, customer behavior, and financial performance.
• Help establish data science as a strategic decision-making capability rather than a reporting or support function.

Lead and Develop the Data Science Team

• Build, lead, and develop a high-performing team of data scientists and quantitative professionals supporting Revenue Strategy.
• Recruit and retain highly skilled data science talent.
• Establish technical, analytical, and communication standards across the team.
• Mentor team members in advanced statistical modeling, forecasting, optimization, and business communication.
• Establish a culture of analytical rigor, intellectual curiosity, accountability, and measurable business impact.
• Ensure the team understands not only how to build models, but why the models behave as they do and how their outputs should inform business decisions.
• Establish clear priorities and effectively manage multiple high-impact initiatives.

Partner Across the Organization

• Work closely with Data & Analytics on data availability, infrastructure, model deployment, and evaluation.
• Partner with Revenue Strategy on pricing, inventory, forecasting, and yield decisions.
• Partner with Marketing to understand and measure demand-generation activities.
• Partner with Ticketing/Box Office teams to understand ticketing operations and inventory dynamics.
• Partner with Finance to ensure analytical recommendations align with financial objectives.
• Present complex analyses and recommendations in intuitive, concise ways to technical and non-technical audiences at all levels of the company.
• Provide exceptional experiences for our guests, partners, and team members, including by adhering to our appearance and presentation guidelines while on-site.

What do you need to succeed?

• 10+ years of relevant experience in data science, statistics, econometrics, revenue management, forecasting, quantitative analytics, operations research, or a related field.
• Demonstrated experience leading data science teams or complex quantitative initiatives.
• Deep expertise in demand forecasting, statistical modeling, predictive analytics, and optimization.
• Demonstrated experience applying data science to pricing, revenue management, yield optimization, or a similar commercial environment.
• Strong understanding of statistical and economic concepts including demand modeling, price elasticity, probability, forecasting, and optimization.
• Strong experience with Python, R, Stata, or another statistical/programming language.
• Strong SQL and data manipulation capabilities.
• Strong experience with Tableau, including developing and communicating analytical insights through dashboards and data visualizations.
• Experience developing and evaluating models that directly inform business and financial decisions.
• Ability to diagnose and clearly explain model behavior, assumptions, drivers, and limitations.
• Exceptional communication and presentation skills, with the ability to explain complex analytical concepts to senior non-technical stakeholders.
• Strong business judgment and ability to connect analytical findings to revenue and financial outcomes.
• Demonstrated ability to work collaboratively across multiple functions while maintaining independent analytical judgment.

Special Requirements

• Experience in entertainment, live events, ticketing, hospitality, travel, airlines, gaming, e-commerce, or another industry involving dynamic demand and perishable inventory.
• Experience with dynamic pricing or sophisticated revenue-management systems.
• Experience building demand forecasting or optimization capabilities from the ground up.
• Experience working with transactional, behavioral, customer, marketing, and inventory data.
• Experience with time-series forecasting, hierarchical forecasting, Bayesian methods, econometrics, or mathematical optimization.
• Experience developing models for new products, experiences, venues, or markets with limited historical data.
• Experience communicating quantitative recommendations to executive leadership.
• Advanced degree in Statistics, Economics, Mathematics, Operations Research, Computer Science, Data Science, or a related quantitative field.

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Pay Range

$200,000—$230,000 USD

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