Ticketmaster gives millions of fans – worldwide – fair and easy access to the biggest and best in live entertainment.

Driven by innovation, unparalleled scalability, and unmatched support, we are the definitive leader in professional ticketing solutions. Over 12,000 artists, teams, and venues around the world trust us to power their amazing performances daily — with more than 500 million tickets sold each year.

We are a diverse team of 6,500+ global employees and a proud division of Live Nation Entertainment (NYSE: LYV), the world’s leading live entertainment company and an organization certified as a Great Place to Work®. Everything we do starts with our passion to dream, design, and deliver the unforgettable experience of live. Because we’re fans too — and live only happens once.

THE JOB

We are seeking a highly motivated and technically skilled Pricing Data Analyst to join our team and turn pricing data into pricing actions. You will be working on some of the most interesting and challenging problems in the sports, arts, family, and concert areas and you will have the opportunity to derive data-driven insights, convert them to actionable recommendations, and communicate those recommendations directly to our clients. You will help achieve these outcomes by building AI workflows that scale our analysis, developing reusable pipelines and frameworks that codify how we approach pricing problems, and partnering with our consulting and managed-service teams on pricing strategy. The ideal candidate brings strong data-minded instincts alongside analytical depth -- someone who moves quickly, builds AI-powered tools, and accelerates how we turn data into decisions.

WHAT YOU WILL BE DOING

Build AI-powered tools and automated workflows that replace recurring manual analytical work to help the team deliver insights faster and at greater scale.

Contribute to and expand upon the team’s data pipelines using Python and SQL to ingest and transform raw pricing, inventory, sales, and demand data into curated views.

Develop scalable analytical frameworks that turn ad-hoc analyses into templated notebooks, processes, and dashboards that can be applied consistently across clients.

Partner with Consulting and Managed Pricing teams to develop a deep understanding of client data to identify trends and opportunities for improvement.

Prototype emerging AI technologies and capabilities -- including LLMs, prompt-driven pipelines, and agentic workflows -- to keep the team’s toolkit at the forefront of what is possible.

Build predictive models for sell-through and yield management optimization, using both traditional statistical methods and machine learning techniques.

Use business intelligence tools to communicate insights through dashboards and reports to internal stakeholders when appropriate.

Collaborate with internal stakeholders and external clients to define project scope, develop hypotheses, execute analyses, and present results.

As business needs evolve, additional responsibilities and projects may be assigned.

WHAT YOU NEED TO KNOW (or TECHNICAL SKILLS)

Bachelor’s Degree in Statistics, Math, Computer Science, Information Technology, Economics, or related fields.

Advanced proficiency in Python and SQL, with experience contributing to data pipelines, automations, and shared code repos.

Hands-on experience building with AI/LLM APIs (e.g., Claude, OpenAI, or equivalent)—writing and chaining prompts, integrating AI into analytical workflows, and shipping AI-assisted tooling.

Strong statistical foundation with experience building predictive models (forecasting, regression, classification, or ML).

Experience in revenue management and pricing optimization, particularly within the live entertainment and ticketing industry, is a plus.

Experience with data warehousing and visualization tools (Databricks, Tableau, Domo, Quicksight, etc.).

Comfortable with PowerPoint and Excel for client deliverables.
Resourceful problem-solving skills.

Strong written and verbal communication skills with the ability to communicate technical processes and results to non-technical audiences.