What are the responsibilities and job description for the Data Scientist position at YO AI Labs?
Must Have:
Python Machine Learning, data science, AWS, Statistical Modeling, Semantic Search, Vector DB, GenAI, SQL
Technical Responsibilities:
Python Machine Learning, data science, AWS, Statistical Modeling, Semantic Search, Vector DB, GenAI, SQL
Technical 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 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.
- 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 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.
- 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.