Data Scientist

Job Summary:

We are seeking an experienced Data Scientist specializing in Experimentation and Causal Inference to lead end-to-end A/B testing and Geo Experiment initiatives for a leading entertainment and media organization. The ideal candidate will combine deep statistical expertise with strong business acumen to deliver strategic insights and influence executive decision-making.

Key Responsibilities:

• Design and Execute Experiments: Lead end-to-end A/B testing initiatives and Geo Experiments, from hypothesis formation and experimental design to statistical analysis and business recommendations.
  
• Advanced Statistical & Causal Inference: Apply deep knowledge of experimental design, regression, classification, and causal inference (difference-in-differences, propensity scores, instrumental variables), and ensure proper assumptions.
  
• Build Scalable Solutions: Develop experimentation and causal inference tools and frameworks that can scale across the organization's businesses.
  
• Deliver Strategic Insights: Partner with stakeholders to identify optimization opportunities and translate complex analytical findings into clear business recommendations.
  
• Influence Executive Decisions: Present findings and recommendations to senior leadership, effectively communicating statistical concepts to non-technical stakeholders.
  

Basic Qualifications:

• Bachelor's degree in Statistics, Economics, Computer Science, Engineering, Mathematics, Physics, or a related field + 7 years of experience with an emphasis on experimentation or causal inference.
  
• Strong background in statistical modeling: regression, classification, time series forecasting, causal inference, and other techniques.
  
• Robust knowledge of causal inference approaches such as propensity scores, synthetic controls, difference-in-differences, doubly robust methods, meta learners, and uplift modeling.
  
• Expertise in A/B test design, execution, statistical modeling, and sophisticated causal inference techniques.
  
• Proficient in conducting sample size calculations, power analysis, and minimum detectable effect estimation.
  
• Experience managing multiple testing scenarios and controlling false discovery rates.
  
• Ability to deploy both Bayesian and frequentist statistical approaches.
  
• Deep understanding of assumptions required for causal inference, including the foundational statistical concepts that underpin the approaches.
  
• Proven ability to manage end-to-end experimentation and causal inference analyses, from initial requirements to impactful outcomes.
  
• Advanced skills in Python and/or R — including development of statistical analysis packages and use of ML frameworks (e.g., scikit-learn, LGBM).
  
• Strong communication skills for translating complex data into actionable narratives and presenting confidently to technical and non-technical audiences, including senior executives.