Lead Data Scientist

Optomi, in partnership with a leading Fortune 500 Media and Entertainment company, is seeking a Lead Data Scientist to join their team! You'll transform complex data into strategic business decisions that shape the future of streaming entertainment. Collaborating closely with cross-functional partners across the business, you'll architect and execute sophisticated experiments that optimize every aspect of the subscriber journey—from initial acquisition through long-term retention and revenue growth.

Job Qualifications:

• 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 inferences, 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.

Job 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, 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 different 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.