Data Scientist - Forecasting (Machine Learning)

TL;DR

Data Scientist - Forecasting (Machine Learning): Building predictive models for player behaviour, engagement, churn, purchase propensity, recommendations, and customer lifetime value with an accent on commercial impact, large-scale behavioural data, and scalable decision support. Focus on translating business problems into robust modelling approaches, developing advanced sequence-based and deep learning methods, and delivering actionable insights with cross-functional stakeholders.

Location: London, United Kingdom

Company

hirify.global is the entertainment and technology brand of Sony Interactive Entertainment, creating gaming hardware, network services, and digital experiences for a global player base.

What you will do

• Develop and deliver machine learning models for churn prediction, purchase propensity, store recommendations, and customer lifetime value.
• Translate commercial and product problems into modelling approaches, selecting and refining suitable methods and features.
• Analyse large-scale behavioural and transactional datasets to identify opportunities for player growth and engagement.
• Collaborate with engineering, product, commercial, finance, and lifecycle stakeholders to deliver robust, scalable solutions.
• Communicate findings and recommendations clearly to technical and non-technical audiences.
• Expand the use of advanced modelling approaches, including embeddings, sequence models, and deep learning.

Requirements

• Experience building predictive models such as churn, propensity, segmentation, or value models in a commercial setting.
• Ability to own problems from definition through modelling, solution delivery, and measurable impact.
• Proficiency in Python and SQL, with familiarity with common data science and machine learning libraries.
• Solid understanding of regression, tree-based models, clustering, model selection, refinement, and tuning for real-world applications.
• Experience working with large datasets and communicating insights across cross-functional teams.
• A strong academic background, typically a Master's or Ph.D. in mathematics, statistics, computer science, or another quantitative field.

Nice to have

• Familiarity with production environments, MLOps, or data pipelines.
• Experience with PySpark or equivalent distributed data processing tools.
• Experience in gaming, e-commerce, or subscription-based products.

Culture & Benefits

• Discretionary bonus opportunity.
• Private medical insurance and dental scheme.
• 25 days of annual holiday.
• On-site gym, subsidised café, free soft drinks, and on-site bar.
• Access to a cycle garage and showers.

Hiring process

• Background checks are conducted at the offer stage, potentially including criminal background checks for some roles.