Data Scientist-Glendale CAHybrid-FTE

Lead end-to-end A/B testing initiatives and geo experiments from hypothesis development and experimental design through statistical analysis and business recommendations.

Apply deep expertise in experimental design regression classification and causal inference methodologies (including difference-in-differences propensity score methods instrumental variables and related techniques) ensuring appropriate assumptions and methodological rigor.

Develop experimentation and causal inference tools frameworks and methodologies that can be scaled across multiple business units and products.

Partner with cross-functional stakeholders to identify optimization opportunities and translate complex analytical findings into clear actionable business recommendations.

Present insights and recommendations to senior leadership effectively communicating statistical concepts and business impact to both technical and non-technical audiences.

Bachelors degree in Statistics Economics Computer Science Engineering Mathematics Physics or another quantitative discipline with 7 years of experience focused on experimentation causal inference or advanced analytics.

Strong background in statistical modeling including regression classification time series forecasting causal inference and related analytical techniques.

Expertise in causal inference methodologies including propensity score methods synthetic controls difference-in-differences doubly robust estimation meta-learners uplift modeling and related approaches.

Extensive experience designing executing and analyzing A/B tests and other experimental methodologies.

Proficiency in sample size calculations statistical power analysis and minimum detectable effect (MDE) estimation.

Experience managing multiple testing scenarios and controlling false discovery rates.

Strong knowledge of both Bayesian and frequentist statistical methods.

Deep understanding of the assumptions underlying causal inference techniques and experimental design.

Demonstrated ability to lead end-to-end experimentation and causal inference projects from problem definition through implementation and business impact.

Advanced programming skills in Python and/or R including development of statistical analysis packages and experience with machine learning frameworks (e.g. scikit-learn LightGBM or similar).

Excellent communication skills with the ability to translate complex analyses into actionable business insights and present findings to technical teams business stakeholders and executive leadership.

Masters degree in Computer Science Statistics Mathematics or another quantitative field with 5 years of relevant experience or a PhD with 3 years of relevant experience in experimentation causal inference or advanced analytics.

Experience with ETL and data engineering including data extraction transformation integration and quality assurance for large-scale analytics.

Experience deploying and monitoring data science solutions in production environments including CI/CD pipelines automated reporting and ongoing experiment or model monitoring.

Familiarity with modern analytics platforms and development tools such as Databricks Jupyter Snowflake GitHub or equivalent technologies.

Strong business acumen with experience applying experimentation and analytics to business strategy customer behavior and market trends.

Proven leadership and stakeholder management experience including influencing cross-functional teams and delivering high-impact analytical initiatives.

Demonstrated ability to adapt quickly to changing priorities while maintaining high standards of quality and execution.

Commitment to fostering a culture of quality innovation and data-driven experimentation.

Experience mentoring team members on statistical methodologies experimentation best practices and the development of scalable analytical solutions.