Director, Pricing Analytics & Data Science

Universal Orlando•Orlando, FL

About The Position

JOB SUMMARY: Leader and subject matter expert in the design, prototyping, and ongoing enhancement of advanced forecasting and price optimization models that transform how UDX (Universal Destinations and Experiences) prices its destinations and experiences. Key leader responsible for applying Machine Learning and Operations Research techniques to maximize revenues while balancing attendance, guest satisfaction, partner satisfaction, and technology capabilities. Will lead team with combining deep expertise in machine learning and operations research with strong collaboration skills, partnering with data engineering, D&T (Digital and Technology) product owner, vendors, and business teams to design, test and enhance mathematical models. MAJOR RESPONSIBILITIES: Price Optimization Models Lead a team of data scientists to design, build, and scale Machine Learning and Operations Research based price optimization models. Apply advanced mathematical models to forecast demand, estimate price elasticity of demand, optimize prices that maximize revenues, and measure impact. Ensure production-worthy solutions that are aligned with evolving business needs and D&T frameworks. Leverage Machine Learning frameworks/libraries and optimization solvers to enable sustainable and scalable models. Act as a primary subject matter expert and leader in pricing data science, advising on methodologies of Machine Learning, Operations Research, and measurement. Create intuitive, interactive decision-support tools and dashboards that make complex results actionable for end users. Collaboration Lead team to collaborate with business stakeholders to identify high-value opportunities and develop a long-term roadmap where Machine Learning and Operations Research models can drive measurable impact. Collaborate with data engineering to ensure robust data pipelines are in place to support price optimization solutions. Partner with D&T in building reusable frameworks for models that are efficient, scalable, and expandable. Champion the use of best practices to enable continuous enhancement of the solutions. Collaborate to establish and enforce standards for adoption and performance of price optimization solutions. Innovation and Research Stay current with innovative price optimization techniques using Machine Learning and Operations Research, applying new methods to real-world use cases. Lead experimentation and proof-of-concept projects to evaluate emerging technologies in Machine Learning and Operations Research for price optimization. Foster a culture of continuous learning, exploration, and innovation for price optimization solutions. Encourage adoption of approaches that enhance scalability, interoperability, and efficiency of models. Understands and actively participates in Environmental, Health & Safety responsibilities by following established UO policy, procedures, training and team member involvement activities. Performs other duties as assigned.

Requirements

Master’s degree with emphasis in Machine Learning, Statistics, Operations Research, Data Science, or a related field. PhD preferred.
10+ years of experience delivering forecasting and optimization solutions for pricing and revenue management.
5+ years of leading analytical team members like data scientists and data engineers.
Strong analytical expertise in demand forecasting models, estimation of price elasticity of demand, dynamic programming, and value measurement.
Proficiency in Python, Databricks, AWS, Snowflake, and AI/ML libraries & frameworks.
Demonstrated record of launching end-to-end solutions in a complex organization.
Strong analytical and critical thinking skills.
Excellent communication skills to convey complex technical concepts to non-technical stakeholders.
Demonstrated willingness to teach others and learn new techniques.
Ability to manage multiple projects required and work independently in a deadline-oriented environment.
Desire to work in a fast-paced environment is required; or equivalent combination of education and experience.
Deep knowledge of modern Machine L earning techniques, demand forecasting models, estimation of price elasticity of demand, and dynamic programming.
Proficiency in Python, Databricks, AWS, Snowflake, and AI/ML libraries & frameworks.
Ability to translate model outputs into user-facing products and operational workflows.
Proven success creating product strategies and roadmaps that realize measurable business value.
Strong financial modeling and experiment-design skills to quantify impact and risk.
Experience leading analytical team members like data scientists and data engineers.
Exceptional influence skills; comfortable driving decisions among senior leaders without formal authority.
Adept at storytelling—able to convey complex concepts to non-technical audiences.
Understanding of data-privacy regulations, responsible-AI guidelines, and model-risk management.

Nice To Haves

PhD preferred.

Responsibilities

Lead a team of data scientists to design, build, and scale Machine Learning and Operations Research based price optimization models.
Apply advanced mathematical models to forecast demand, estimate price elasticity of demand, optimize prices that maximize revenues, and measure impact.
Ensure production-worthy solutions that are aligned with evolving business needs and D&T frameworks.
Leverage Machine Learning frameworks/libraries and optimization solvers to enable sustainable and scalable models.
Act as a primary subject matter expert and leader in pricing data science, advising on methodologies of Machine Learning, Operations Research, and measurement.
Create intuitive, interactive decision-support tools and dashboards that make complex results actionable for end users.
Lead team to collaborate with business stakeholders to identify high-value opportunities and develop a long-term roadmap where Machine Learning and Operations Research models can drive measurable impact.
Collaborate with data engineering to ensure robust data pipelines are in place to support price optimization solutions.
Partner with D&T in building reusable frameworks for models that are efficient, scalable, and expandable.
Champion the use of best practices to enable continuous enhancement of the solutions.
Collaborate to establish and enforce standards for adoption and performance of price optimization solutions.
Stay current with innovative price optimization techniques using Machine Learning and Operations Research, applying new methods to real-world use cases.
Lead experimentation and proof-of-concept projects to evaluate emerging technologies in Machine Learning and Operations Research for price optimization.
Foster a culture of continuous learning, exploration, and innovation for price optimization solutions.
Encourage adoption of approaches that enhance scalability, interoperability, and efficiency of models.
Understands and actively participates in Environmental, Health & Safety responsibilities by following established UO policy, procedures, training and team member involvement activities.
Performs other duties as assigned.