$ cat jobs/data-scientist-incremental-player-value-playstation-global-8f3f589667b1.json
Data Scientist – Incremental Player Value
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 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. Department Overview: 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 the drivers of long-term player value and enabling better business decisions through robust forecasting and insight generation. As a Causal Insights Specialist, you will help transform how PlayStation measures and optimises player value by identifying the true impact of products, features, and commercial interventions on long-term player outcomes. You will develop causal measurement frameworks and apply causal inference techniques to distinguish correlation from causation, enabling more effective investment decisions across the player lifecycle. This role is ideal for someone who is passionate about understanding why outcomes occur, enjoys solving complex business problems through rigorous analysis, and can translate causal insights into actionable recommendations for stakeholders. What you'll be doing: Apply causal inference methodologies to measure the incremental impact of player experiences, lifecycle interventions, product features, and commercial initiatives on Customer Lifetime Value (CLV). Design, analyse, and interpret experiments, quasi-experiments, and observational studies
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