Data Scientist - Measurement, Experimentation & Causal Inference
Salary not listed • United Kingdom, London • Full-time
Posted 3mo ago
This job has been open for more than 60 days. It may no longer be actively hiring, so check with the company before putting a lot into an application.
About the job
Sony Interactive Entertainment (SIE) is seeking a Data Scientist to measure the impact of PlayStation's products, features, and commercial initiatives through experimentation, causal inference, and applied data science. This role operates at the intersection of data science, machine learning, and product strategy, applying robust analytical techniques to solve complex business problems and support evidence-based decision-making. You will go beyond traditional A/B testing, utilizing modern causal inference, statistical modeling, and machine learning to evaluate initiatives where controlled experiments are challenging, collaborating closely with various teams to deliver high-quality measurement and insights. This is an opportunity to work on high-impact initiatives affecting millions of players while developing expertise in experimentation, causal inference, and applied data science.
Responsibilities
- Apply measurement methodologies across experimentation, causal inference, and advanced analytics to evaluate product and commercial initiatives.
- Design and execute robust measurement approaches using randomized experiments, quasi-experimental methods, and observational causal inference.
- Apply statistical, machine learning, and AI techniques to solve complex measurement challenges and generate actionable business insights.
- Develop reusable analytical solutions, dashboards, and code to improve measurement quality, consistency, and efficiency.
- Communicate analytical findings and recommendations clearly to technical and non-technical stakeholders.
- Collaborate with engineering teams to provide feedback on experimentation and measurement capabilities, improving tooling and workflows.
- Contribute to the development and adoption of best practices for experiment design, statistical analysis, and causal measurement.
- Partner with product managers, engineers, analysts, and business stakeholders to identify high-impact measurement opportunities.
- Stay current with emerging developments in statistics, machine learning, and AI to enhance measurement capabilities.
Requirements
- Master's degree (or equivalent industry experience) in a quantitative discipline (Statistics, Economics, Mathematics, Computer Science, Data Science); PhD preferred.
- 3+ years of industry experience applying experimentation, causal inference, statistical modeling, or machine learning to business problems.
- Good understanding of experimental design, A/B testing, and modern causal inference methods.
- Experience applying data science, machine learning, and statistical modeling to analyze complex datasets and generate insights.
- Strong proficiency in Python and SQL for analytical workflows.
- Experience developing robust analytical solutions using sound statistical principles and software engineering best practices.
- Strong stakeholder management and communication skills, with the ability to explain technical concepts to diverse audiences.
- Curiosity, creativity, and a pragmatic approach to solving ambiguous business problems.
- Collaborative mindset with a passion for continuous learning.