Data Scientist - Measurement, Experimentation & Causal Inference

Posted: 2026-07-28

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.

As a Data Scientist, you will help measure the impact of PlayStation's products, features and commercial initiatives through experimentation, causal inference and applied data science.

Working at the intersection of data science, machine learning, product and commercial strategy, you will apply robust analytical techniques to solve complex business problems, generate actionable insights and support evidence-based decision-making across PlayStation.

This role goes beyond traditional A/B testing. You will apply modern causal inference, statistical modelling and machine learning techniques to evaluate initiatives where controlled experiments are difficult or impossible, collaborating closely with product, engineering, analytics and business teams to deliver high-quality measurement and insights.

This is an opportunity to work on high-impact initiatives affecting millions of PlayStation players while developing your expertise in experimentation, causal inference and applied data science.

- Apply measurement methodologies across experimentation, causal inference and advanced analytics to evaluate product and commercial initiatives.

- Design and execute robust measurement approaches across randomised experiments, quasi-experimental methods and observational causal inference where controlled experimentation is impractical.

- Apply statistical, machine learning and AI techniques to solve complex measurement challenges and generate actionable business insights.

- Apply advanced data science and machine learning techniques where appropriate to complement experimentation and support complex business decisions.