Data Scientist

Job Title:

Senior Data Scientist – Experimentation & Causal Inference

Job Type: Contract (12 Months)

Location:

Glendale, CA, USA

Work Hours:

8 hours/day | 40 hours/week

Experience

Required:

5 – 20 Years

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.

• 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.

• 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.

• 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 Git Hub.
• 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.