Data Scientist – Incremental Player Value
Core
Identify the true impact of products, features, and commercial interventions on long-term player outcomes using causal inference to enable better investment decisions.
Role type
Causal Insights Specialist (Data Scientist)
Builds
Causal measurement frameworks and insights for player lifecycle optimization
Domain
Gaming / Entertainment
Deliverable
production ML models | dashboards & analysis
Required skills
causal inference, experimental design, A/B testing, Python, SQL, statistics, large-scale data analysis
Preferred skills
Double Machine Learning, Causal Forests, Meta-Learners, Uplift Models, Bayesian methods, PySpark, MLOps
Technologies
Python, SQL, PySpark
Responsibilities
Apply causal inference to measure incremental impact on Customer Lifetime Value (CLV); Design and interpret experiments and observational studies; Develop frameworks for measuring incremental CLV and uplift; Collaborate with stakeholders to improve decision-making; Analyze large-scale behavioral and transactional datasets; Translate complex analyses into actionable recommendations; Contribute to the evolution of the CLV framework
Seniority
Mid-to-Senior, hands-on IC
