Own the analytical engine of our commercialization system: discounting, pricing, regional sales, lifecycle performance, and portfolio analysis across our catalog of hundreds of games
Design and execute experiments in close partnership with our Business Operations Manager, who owns the discount calendar and discount strategy — you bring the analytical rigour, she owns the process and implementation
Build and maintain reporting for physical and digital sales across our publishing labels, including our newly acquired Thunderful portfolio
Take per-game analyses off the Director of Commercialization's plate and execute them consistently and on schedule
Build business cases for commercial decisions including platform deals, pricing changes, bundle strategies, and portfolio opportunities (ports, streaming, cloud gaming, physical partnerships)
Develop metrics to measure the holistic performance of our commercialization system at every level — game, catalog, and company
Inherit and improve AI-powered decision-support tools, including a discount depth recommender and discount scheduler
Identify and build additional automations that reduce manual analytical work across the team — with the goal of progressively freeing up capacity for higher-order problems
Serve as a thought partner on how AI tooling can augment work across departments beyond Commercialization
Work in close partnership with our Business Operations Manager: you own analytical depth, she owns process and execution — the handoff between you should be clean and mutually reinforcing
Present findings and recommendations to the Director of Commercialization and, where relevant, to executive leadership
Requirements:
3–5 years of relevant experience in analytics, commercial strategy, or a related field; games industry background a plus but not required
Strong statistical foundations — you know where analyses break down, you hunt for confounded variables and aggregation errors before anyone asks you to, and you can explain your methodology clearly under scrutiny
Financial modelling skills, including comfort building business cases from first principles
Demonstrated ability to work with large, messy datasets and produce clean, credible outputs
Familiarity with AI tools and a genuine interest in building automations that augment analytical work — you don't need to be an engineer, but you should be able to build and iterate on AI-assisted workflows
High autonomy: you receive a problem, you figure out the approach, and you come back with a proposal. You don't need the solution handed to you
Intellectual honesty — you'd rather tell someone their hypothesis is wrong than confirm it with a flawed analysis.