Responsibilities

• Own the end-to-end Forecasting process from a data, modelling, and technology perspective, ensuring models, tools, QA routines, and monitoring are reliable, scalable, and business-relevant.
• Design internal tools, QA workflows, and reusable modelling components that help the Forecasting team build, test, iterate, and deploy forecasting and Marketing Mix Modelling solutions efficiently.
• Develop simulation and scenario planning tools that help marketing teams evaluate media investment, campaign strategies, pricing, promotions, and potential sales outcomes.
• Support and improve the model performance monitoring process, including tracking forecast accuracy, identifying systematic bias, capturing stakeholder feedback, and feeding these learnings back into model development.
• Success will be measured by the quality, adoption, and business impact of delivered tools and modelling processes, including improved forecast reliability, faster scenario turnaround, stronger monitoring, and better marketing decision support.

Requirements

• Demonstratable current proficiency in mathematics (e.g. A-level Mathematics with grade A, A+ or equivalent).
• Strong delivery mindset, with the ability to work under tight deadlines and consistently drive business impact.
• Proficiency in Python, including modular code design using functions and classes, and experience with version control tools such as Git.
• Experience applying statistical modelling to business problems, including model validation, performance monitoring, and communication of model limitations.
• Experience developing internal tools or analytical applications using frameworks such as Flask, FastAPI, or similar.
• Familiarity with cloud computing platforms (e.g. Google Cloud).
• Understanding of game industry dynamics, including genre-specific sales patterns, platform strategies, long-tail revenue behaviour, and the impact of discounting and marketing campaigns.
• Ability to translate business logic into data-driven forecasting frameworks.
• Experience with Marketing Mix Modelling or media effectiveness analysis, including understanding how media spend, channel mix, seasonality, promotions, pricing, and external factors can influence sales or player behaviour.
• Strong communication skills, with ability to explain complex concepts to both technical and non-technical stakeholders.

Preferred

• Ability to build and evaluate Bayesian models using PyMC or Stan, with a focus on real-world forecasting applications. Formal work experience not required.
• Experience in pricing analytics, demand modelling, or revenue forecasting within entertainment or consumer industries.
• Experience of managing/mentoring junior members.