Key Responsibilities

• Design, build, and maintain pricing and optimisation models supporting airline dynamic pricing use cases.
• Perform detailed analysis of demand, elasticity, constraints, and commercial objectives.
• Develop and validate features suitable for real-time and batch decisioning.
• Define offline evaluation strategies and success metrics aligned with airline commercial goals.
• Design, analyse, and interpret experiments such as A/B tests, shadow runs, and back-testing.
• Translate experimental results into clear recommendations for Product and client stakeholders.
• Own model behaviour during UAT and go-live phases, including rapid diagnosis and remediation.
• Work closely with Engineering to productionise models with appropriate performance and latency characteristics.
• Support monitoring of model outputs, data quality, and drift.
• Act as a data science point of contact in client-facing discussions related to pricing behaviour and performance.
• Explain model outcomes, trade-offs, and limitations clearly to non-technical audiences.
• Apply disciplined, reproducible data science practices including peer review and documentation.
• Contribute to shared modelling patterns, libraries, and evaluation approaches.

Required Experience

• Background in pricing, revenue management, demand modelling, or optimisation (airline or adjacent industries preferred).
• Solid grounding in statistics, machine learning, and experimental design.
• Experience working with high-volume, transactional or near real-time systems.
• Proven ability to work directly with clients and senior stakeholders.
• Comfortable collaborating with software engineers on production systems.
• Calm, structured approach under UAT and go-live pressure.

Desirable Experience

• Airline dynamic pricing or revenue management domain experience.
• Experience with model explainability and governance in commercial settings.
• Familiarity with MLOps practices, monitoring, and model lifecycle management.
• Exposure to large-scale experimentation platforms or causal inference methods.

Working Model

Hybrid and outcome-focused. Strong emphasis on reliability, transparency, and client confidence. Small, high-ownership squad with close collaboration across Data Science, Engineering, and Product. Scope expected to evolve as the Pricing AI product and team mature.

About [Employer hidden — sign up to reveal]

[Employer hidden — sign up to reveal] is a market leader in airline e-commerce solutions. [Employer hidden — sign up to reveal]'s Stellex product suite, launched in 2024, gives airlines the tools they need to drive revenue and profit as digital retailers. [Employer hidden — sign up to reveal] has a strong track record working with some of the most innovative airline brands worldwide, such as Air China, Air Macau, Air Transat, Aer Lingus, easyJet, and Edelweiss. The Group is headquartered in Dublin, Ireland, and maintains offices across Europe, the Americas, and Asia. Learn more at www.[Employer hidden].com.