Location
London

Hours
Full Time

Salary
Negotiable

About the Role
Pricing is one of the most direct levers ATG Entertainment has for revenue growth, and the Pricing Data Science team has already demonstrated this through several pricing models now live in production. As the team expands across multiple growth areas, this role is central to that expansion: working alongside Revenue Managers to build proofs of concept that ensure no revenue opportunity goes unexplored and helping size and prioritise the next models on the roadmap. The role sits at the intersection of Revenue Management, CRM & Marketing, and ML Engineering, requiring someone who can operate credibly across all three domains.

In the near term, the successful candidate will add delivery capacity to two live workstreams: the Sales-facing revenue quick-wins programme and the model backlog, delivering output from week one without a long ramp-up period.

Key responsibilities include:
- Contributing directly to the live revenue quick wins workstream by translating commercial rules and constraints supplied by Sales into working model logic.
- Owning discrete items from the model backlog end-to-end: specifying, building, validating, and shipping to production.
- Applying causal inference to pricing decisions with rigor, using methods such as difference-in-differences, CausalImpact/BSTS-style approaches, or comparable quasi-experimental designs, accounting for suppression and substitution effects rather than naive before/after comparisons.
- Running two to three workstreams concurrently with minimal supervision and proactively flagging blockers and scope risks early.

Experience
- Proven applied experience in pricing or revenue management analytics within a genuinely high-frequency, perishable-inventory, demand-variable domain such as live events/ticketing, airline, hotel, sports, or transport revenue management. Ticketing/entertainment-sector experience is strongly preferred but not mandatory.
- Hands-on experience applying causal inference to real pricing or promotional decisions, with the ability to describe how confounding and selection bias were handled on actual datasets.
- Strong applied statistics/econometrics skills sufficient to defend specific model choices (e.g., why OLS, why a GLM, why not a black-box model) rather than defaulting to whichever library is fastest.
- Demonstrated ability to switch between unrelated problems within a single week without requiring long re-orientation.

About you
- Comfortable working across Revenue Management, CRM & Marketing, and ML Engineering.
- Self-motivated with the ability to manage multiple workstreams concurrently and communicate risks proactively.
- Passionate about delivering practical, impactful data science solutions in a fast-paced environment.

Qualifications
- Desirable but not essential:
- Direct pricing or revenue management experience in live entertainment or ticketing.
- Experience with AWS ML stack (SageMaker in particular) or demonstrated ability to quickly translate from equivalent platforms.
- Experience with seat-map or other inventory-constrained demand modelling.
- Exposure to LLM/GenAI tooling within production analytics workflows.
- Proficiency in Python.