Job Specification: Data Scientist – Pricing Modelling (Sport)

Overview

We are seeking a highly skilled Data Scientist with proven experience in pricing modelling within the sports industry. The ideal candidate will be responsible for building, optimising, and deploying data-driven pricing models that help maximise revenue, improve forecasting accuracy, and support strategic commercial decisions. This role will work closely with commercial, product, finance, and analytics teams to deliver impactful insight and pricing recommendations.

Key Responsibilities

Pricing Modelling & Analytics

• Develop, own, and optimise dynamic pricing models across sports products (tickets, memberships, hospitality, broadcasting, merchandise, or gaming—depending on organisation).
• Build predictive models for demand forecasting, elasticity analysis, and customer behaviour.
• Run simulations and scenario modelling to guide pricing strategy.
• Maintain model accuracy through performance monitoring, retraining, and back-testing.

Data Science & Engineering

• Work with large datasets from multiple sources including ticketing systems, CRM data, sales systems, attendance data, and third-party partners.
• Build robust data pipelines and ensure high data quality and reproducibility.
• Deploy models into production environments (e.g., via APIs, automated workflows, or BI tools).

Insight & Strategy

• Translate modelling outputs into clear commercial recommendations for non-technical stakeholders.
• Present findings to senior leadership, supporting decision-making with evidence-based insight.
• Collaborate with marketing and commercial teams to understand demand drivers and pricing levers.
• Evaluate the impact of pricing changes and provide post-event analysis.

Innovation

• Explore new modelling techniques such as reinforcement learning, dynamic optimisation, or real-time price adjustment.
• Stay current with trends in sports analytics, pricing science, machine learning, and demand forecasting.

Required Skills & Experience

• 3+ years in data science, analytics, or econometrics roles (pricing strongly preferred).
• Strong experience with pricing models, demand forecasting, price elasticity, or dynamic pricing algorithms.
• Proficiency in Python (Pandas, NumPy, Scikit-Learn; bonus: PyTorch/TF).
• Experience using SQL and working with relational databases or cloud data warehouses.
• Solid statistical knowledge: regression modelling, time series forecasting, Bayesian methods, optimisation techniques.
• Experience deploying models to production (e.g., Docker, cloud platforms, CI/CD pipelines).
• Ability to design and interpret A/B tests or controlled experiments.
• Excellent stakeholder communication skills, especially with commercial teams.

Desirable Skills

• Experience in the sports industry (ticketing, memberships, fan behaviour, sports betting, E-commerce).
• Familiarity with sports-specific datasets or pricing environments.
• Knowledge of BI tools (e.g., Looker, Power BI, Tableau).
• Understanding of revenue management principles (yield management, inventory controls).
• Background in applied mathematics, economics, statistics, or a related field.

Personal Attributes

• Commercially minded with a passion for applying data science to real business problems.
• High attention to detail with strong analytical rigour.
• Able to manage multiple projects and work in fast-paced environments.
• Curious, proactive, and comfortable challenging assumptions with data.

What We Offer

• Opportunity to shape a cutting-edge pricing strategy in a high-growth sports environment.
• Close collaboration with data engineers, analysts, commercial teams, and leadership.
• A culture that values experimentation, innovation, and data-driven decision making.
• Professional development opportunities across data science, engineering, and sports analytics.