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About The Company
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 PlayStation 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 PlayStation brand, a name synonymous with entertainment excellence and creativity.
About The Role
At PlayStation, 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 at scale. You will work in cross‑functional teams to translate player behaviour into actionable strategies that drive measurable improvements in player engagement and value. This role is ideal for an individual who is comfortable owning problems end‑to‑end — from framing and modelling through to delivering impact — and partnering with stakeholders to support data‑informed decision‑making.
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Qualifications
- A strong academic background, typically a Master’s or Ph.D. in Mathematics, Statistics, Computer Science, or a related quantitative/technical field.
- Experience building predictive models such as churn, propensity, segmentation, or value modeling in a commercial setting.
- Proficiency in Python and SQL, with familiarity with common data science and machine learning libraries.
- Solid understanding of machine learning techniques including regression, tree-based models, clustering, and when to apply them.
- Ability to independently take a problem from definition through to solution and delivery, demonstrating initiative and ownership.
- Strong analytical, problem-solving, and data manipulation skills.
- Excellent communication skills, capable of articulating insights clearly to both technical and non-technical audiences.
- Experience working with large datasets to generate actionable insights.
- Interest in modern machine learning approaches such as embeddings, sequence models, and deep learning, with a willingness to learn and apply these methods.
Responsibilities
- 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 complex business problems into suitable modeling approaches, selecting appropriate features and methods to deliver measurable impact.
- Work with large-scale behavioural and transactional data to identify patterns and opportunities for player growth and engagement.
- Collaborate with cross-functional teams including engineering, product, and commercial stakeholders to ensure solutions are scalable, robust, and aligned with business objectives.
- Partner with stakeholders across commercial, finance, and lifecycle teams to support decision-making processes using data-driven insights.
- Communicate findings and recommendations effectively to both technical and non-technical audiences.
- Continuously develop expertise in advanced modeling techniques such as deep learning and sequence-based methods to solve increasingly complex problems.
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Benefits
- Discretionary bonus opportunity
- Private Medical Insurance
- 25 days holiday per year
- On-site gym facilities
- Free soft drinks
- Access to cycle garage and showers
Equal Opportunity
Sony is an Equal Opportunity Employer. All persons will receive consideration for employment without regard to gender (including gender identity, gender expression, and gender reassignment), race (including colour, nationality, ethnic or national origin), religion or belief, marital or civil partnership status, disability, age, sexual orientation, pregnancy, maternity or parental status, trade union membership, or membership in any other legally protected category.
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