Data Scientist

Job Description

Job Title: Senior Data Scientist – Experimentation & Causal Inference

Industry: Entertainment / Media / Publishing

Job Type: Contract (12 Months)

Location: Glendale, CA, USA

Work Hours: 8 hours/day | 40 hours/week

Experience Required: 5 – 20 Years

Job Summary:

We are seeking an experienced Data Scientist specializing in Experimentation and Causal Inference to lead end-to-end A/B testing and Geo Experiment initiatives for a leading entertainment and media organization. The ideal candidate will combine deep statistical expertise with strong business acumen to deliver strategic insights and influence executive decision-making.

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, and 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 the organization'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.
  

Basic Qualifications:

• Bachelor's degree 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 inference, 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.
  

Requirements

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 the 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.
  

Requirements
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 the 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.