Job Description

Must Haves:

Python Machine Learning, data science, AWS, Statistical Modeling, Semantic Search, Vector DB, GenAI, SQL

Qualifications:

Bachelors 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 modelling: 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.

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, causal inference and ensure proper assumptions.

Build Scalable Solutions: Develop experimentation and causal inference tools and frameworks that can scale across Disney'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.

Preferred Qualifications:

MS in computer science, statistics, math or a related quantitative field +5 years of relevant experience OR PhD + 3 years of relevant experience with an emphasis on experimentation or causal inference.
Experience with ETL and data engineering: data extraction, transformation, integration, and quality controls for analytics at scale.
Skilled in production deployment and monitoring of data science solutions, including CI/CD pipelines, automated reporting, and ongoing experiment/model monitoring.
Familiarity with data platforms and applications such as Databricks, Jupyter, Snowflake, and Github.
Strong strategic business insight, preferably in subscription-based business models, with ability to apply experimentation and analytics to market trends and consumer insights.
Proven track record of leadership and stakeholder/project management, including influencing cross-functional teams and delivering high-impact outcomes.
Adept at adapting quickly to shifting priorities in a fast-moving environment while maintaining quality.
Drive and maintain a culture of quality, innovation and experimentation.
Demonstrated experience mentoring colleagues on best practices and technical concepts for building large scale solutions.