Data Scientist

YO AI Labs; Glendale (AZ); On-site; USD 120,000 - 180,000; Full time

Job summary

YO AI Labs is seeking a Data Scientist to lead end-to-end experimentation and causal inference analyses in Glendale, AZ. The role focuses on advanced statistical methods, scalability, and actionable business insights.

The ideal candidate will have 7+ years in experimentation or causal inference, strong skills in Python/R and ML frameworks, and excellent communication to executives. On-site position with a focus on GenAI and analytics at scale.

Qualifications

• Bachelor's degree + 7 years experience with experimentation or causal inference.
• Strong background in statistical modelling: regression, classification, time series forecasting, causal inference.
• Robust knowledge of causal inference approaches: propensity scores, synthetic controls, diff-in-diffs, doubly robust, meta learners, uplift modeling.
• Expertise in A/B test design and advanced causal inference techniques.
• Proficient in sample size calculations, power analysis and minimum detectable effect estimation.
• Experience managing multiple testing scenarios and controlling false discovery rates; ability to deploy Bayesian and frequentist approaches.
• Deep understanding of assumptions for causal inferences and foundational statistics.
• Proven ability to manage end-to-end experimentation and causal inference analyses.

Responsibilities

• Design and Execute Experiments: Lead end-to-end A/B testing initiatives and Geo Experiments.
• Advanced Statistical & Causal Inference: Apply deep knowledge of experimental design and causal methods.
• Build Scalable Solutions: Develop tools/frameworks for experimentation and causal inference across businesses.
• Deliver Strategic Insights: Translate analytics into clear business recommendations for stakeholders.
• Influence Executive Decisions: Present findings to senior leadership and explain statistics to non-technical audiences.

Skills

Python

Machine Learning

Data Science

AWS

Statistical Modeling

Causal Inference

A/B Testing

Bayesian & Frequentist

SQL

GenAI

Semantic Search

Vector DB

Python packages (scikit-learn)

ML Frameworks (e.g., scikit-learn, LGB

Education

Bachelor's degree in Statistics/ Economics/CS/Engineering/Math/Physics

7+ years experience with experimentation or causal inference

Tools

Databricks

Jupyter

Snowflake

GitHub

Job description

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

Job Description

• Job title: Data Scientist
• Experience: 5-15 Years
• Location: Glendale, USA
• Job Type: Full-time

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 modelling, 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.