Data Scientist (Incremental Player Value)

Join PlayStation as a Data Scientist specializing in Incremental Player Value. In this role, you will focus on understanding the drivers of long-term player value and enabling better business decisions through robust forecasting and insight generation. You will apply causal inference methodologies to measure the incremental impact of player experiences, product features, and commercial initiatives on Customer Lifetime Value (CLV). This position requires strong quantitative skills, experience with causal inference techniques, and the ability to translate complex analyses into actionable recommendations.

• Appliquer des méthodologies d'inférence causale pour mesurer l'impact incrémental des expériences des joueurs, des interventions de cycle de vie, des caractéristiques des produits et des initiatives commerciales sur la valeur à vie du client (CLV).
• Concevoir, analyser et interpréter des expériences, des quasi-expériences et des études d'observation pour répondre à des questions stratégiques.
• Collaborer avec des équipes interfonctionnelles et des parties prenantes commerciales pour identifier les opportunités où la mesure causale peut améliorer la prise de décision et l'allocation des ressources.
• Exposure to experimental design and A/B testing
• You’re intellectually curious, analytical, and passionate about understanding cause-and-effect relationships in complex systems. You bring strong quantitative skills and enjoy applying rigorous methods to high-impact business challenges
• Excellent communication and stakeholder management skills, with the ability to explain complex methodologies and findings to diverse audiences
• Strong problem-solving skills and a structured approach to tackling data challenges
• Strong understanding of causal inference methods
• Strong foundation in statistics
• Ability to independently frame business questions, select appropriate methodologies, and deliver actionable recommendations
• Ability to balance methodological rigour with practical business considerations
• Experience working with large datasets and translating findings into business decisions
• Experience applying causal inference techniques in a commercial, product, marketing, or research environment
• Proficiency in Python and SQL, with experience using statistical and causal inference libraries
• A strong academic background, typically a Master’s or Ph.D. in a quantitative or technical field (e.g. Mathematics, Statistics, Economics, Econometrics, Computer Science, or a related quantitative field.)
• Experience evaluating incrementality, or uplift using experimental data
• Knowledge of Bayesian methods for causal analysis and decision-making
• Familiarity with modern causal machine learning techniques such as Double Machine Learning, Causal Forests, Meta-Learners, and Uplift Models
• Experience in gaming, e-commerce, or subscription-based products
• Experience working with large-scale data using PySpark or equivalent distributed data processing tools
• Familiarity with production environments, MLOps, or data pipelines