Data Scientist - Measurement, Experimentation & Causal Inference

About this role

<div class="content-intro"><p><strong>Why Sony Interactive Entertainment?</strong></p> <p>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.</p></div><h1><span style="font-size: 12pt;">Role Overview:</span></h1> <p><span style="font-size: 12pt;">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.</span></p> <p><span style="font-size: 12pt;">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.</span></p> <p><span style="font-size: 12pt;">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.</span></p> <p><span style="font-size: 12pt;">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.</span></p> <h1><span style="font-size: 12pt;"><strong>What You'll Be Doing:</strong></span></h1> <ul> <li style="font-size: 12pt;"><span style="font-size: 12pt;">Apply measurement methodologies across experimentation, causal inference and advanced analytics to evaluate product and commercial initiatives.</span></li> <li style="font-size: 12pt;"><span style="font-size: 12pt;">Design and execute robust measurement approaches across randomised experiments, quasi-experimental methods and observational causal inference where controlled experimentation is impractical.</span></li> <li style="font-size: 12pt;"><span style="font-size: 12pt;">Apply statistical, machine learning and AI techniques to solve complex measurement challenges and generate actionable business insights.</span></li> <li style="font-size: 12pt;"><span style="font-size: 12pt;">Apply advanced data science and machine learning techniques where appropriate to complement experimentation and support complex business decisions.</span></li> <li style="font-size: 12pt;"><span style="font-size: 12pt;">Contribute to the development and adoption of best practices for experiment design, statistical analysis and causal measurement.</span></li> <li style="font-size: 12pt;"><span style="font-size: 12pt;">Partner closely with product managers, engineers, analysts and business stakeholders to identify high-impact measurement opportunities and ensure product and commercial decisions are supported by rigorous evidence.</span></li> <li style="font-size: 12pt;"><span style="font-size: 12pt;">Collaborate with engineering teams by providing feedback on experimentation and measurement capabilities to improve tooling and workflows.</span></li> <li style="font-size: 12pt;"><span style="font-size: 12pt;">Develop reusable analytical solutions, dashboards and code that improve the quality, consistency and efficiency of measurement.</span></li> <li style="font-size: 12pt;"><span style="font-size: 12pt;">Communicate analytical findings and recommendations clearly to technical and non-technical stakeholders.</span></li> <li style="font-size: 12pt;"><span style="font-size: 12pt;">Stay current with emerging developments in statistics, machine learning and AI, identifying opportunities to improve measurement capabilities and analytical workflows.</span></li> <li style="font-size: 12pt;"><span style="font-size: 12pt;">Contribute to a culture of evidence-based decision-making by sharing knowledge, collaborating across teams and promoting analytical best practices.</span></li> </ul> <h1><span style="font-size: 12pt;"><strong>What We're Looking For:</strong></span></h1> <h2><span style="font-size: 12pt;"><strong>Required:</strong></span></h2> <ul> <li style="font-size: 12pt;"><span style="font-size: 12pt;">Master's degree (or equivalent industry experience) in Statistics, Economics, Mathematics, Computer Science, Data Science or another quantitative discipline. PhD preferred.</span></li> <li style="font-size: 12pt;"><span style="font-size: 12pt;">3+ years of industry experience applying experimentation, causal inference, statistical modelling, machine learning or related analytical techniques to solve business problems.</span></li> <li style="font-size: 12pt;"><span style="font-size: 12pt;">Good understanding of experimental design, A/B testing and modern causal inference methods, including quasi-experimental approaches.</span></li> <li style="font-size: 12pt;"><span style="font-size: 12pt;">Experience applying data science, machine learning and statistical modelling techniques to analyse complex datasets and generate actionable insights.</span></li> <li style="font-size: 12pt;"><span style="font-size: 12pt;">Strong proficiency in Python and SQL for analytical workflows.</span></li> <li style="font-size: 12pt;"><span style="font-size: 12pt;">Experience developing robust analytical solutions using sound statistical principles and software engineering best practices.</span></li> <li style="font-si