Data Scientist - CLV

Why Sony Interactive Entertainment

Sony Interactive Entertainment isn’t just the Best Place to Play — it’s also the Best Place to Work. Sony Interactive Entertainment (SIE) is the company behind the Play Station brand. As a subsidiary of Sony Group Corporation, we’re part of a proud legacy of innovation and excellence. SIE is a dynamic technology company, delivering cutting‑edge hardware and network services to more than 100 million people and an entertainment leader, home to some of the most beloved and recognizable intellectual properties (IP) in the world.

Our role at SIE is to create and nurture the experiences under the Play Station brand, a name synonymous with entertainment excellence and creativity.

At Play Station, Data Science plays a critical role in shaping how we invest in, retain, and delight our global player base. The CLV team focuses on understanding player behaviour and driving more effective engagement across the player lifecycle — from acquisition and onboarding through to retention, monetisation, and long‑term value. As a Data Scientist, you will develop models and insights that help drive more personalised player experiences and inform commercial and product decisions  will work in cross‑functional teams to translate player behaviour into actionable strategies that drive measurable improvements in player engagement and value.

• Own the development and delivery of machine learning models for use cases such as churn prediction, purchase propensity, store recommendations, and customer lifetime value.
• Translate business problems into modelling approaches, selecting appropriate methods and features to deliver measurable impact.
• Work with large‑scale behavioural and transactional data to uncover patterns and opportunities for player growth and engagement.
• Collaborate within cross‑functional teams, including engineering, product, and commercial stakeholders, to ensure solutions are robust, scalable, and aligned with business needs.
• Partner with stakeholders across commercial, finance, and lifecycle teams to support decision‑making with data‑driven insights.
• Clearly communicate findings and recommendations to both technical and non‑technical audiences.
• Develop and expand your understanding of more advanced modelling approaches (e.g. deep learning and sequence‑based methods) as part of solving increasingly complex problems.

• You’re curious, analytical, and a strong problem‑solver, with a structured approach to tackling business problems.
• You bring strong foundations in modelling and data manipulation, and are motivated by applying these to impactful, commercial problems.
• Experience building predictive models (e.g. churn, propensity, segmentation, or value modelling) in a commercial setting.
• Ability to independently take a problem from definition through to solution and delivery, demonstrating initiative and ownership.
• Proficiency in Python and SQL, and familiarity with common data science and ML libraries.
• Solid understanding of machine learning techniques (e.g. regression, tree‑based models, clustering) and when to apply them, including how to refine and tune them for real‑world problems.
• Strong communication and collaboration skills, with the ability to clearly articulate insights and work effectively with cross‑functional stakeholders.
• Awareness of modern machine learning approaches (e.g. embeddings, sequence models, deep learning) and interest in applying them to real‑world problems.
• Experience working with large datasets to generate actionable insights.
• A strong academic background, typically a Master’s or Ph.D. in a quantitative or technical field (e.g. Mathematics, Statistics, Computer Science).

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

• Discretionary bonus opportunity
• Private Medical Insurance
• Dental Scheme
• 25 days holiday per year
• On Site Gym
• Subsidised Café
• Free soft drinks
• On site bar
• Access to cycle garage and showers

Please…