Senior Data Scientist (Experimentation & Causal Inference)

Overview

An opportunity is open for a seasoned Senior Data Scientist focused on experimentation and causal inference. In this position, you will shape the design, execution, and interpretation of large-scale experiments and analytical studies that guide business strategy. The role works closely with product, engineering, and business stakeholders to turn complex data into practical decisions.

What you will do

• Own experimentation programs from the initial hypothesis through deployment, analysis, and final business recommendations, including A/B tests and geo-based experiments.
• Create statistically sound experiment designs and choose the right methodology for each business question.
• Estimate sample sizes, statistical power, and minimum detectable effect to support reliable testing.
• Handle multiple hypothesis testing and use false discovery rate control methods where needed.
• Use Bayesian and frequentist techniques based on the analytical context.
• Apply advanced causal inference methods such as difference-in-differences, propensity score matching, synthetic controls, instrumental variables, doubly robust estimation, meta learners, and uplift modeling.
• Check and validate assumptions to ensure statistical integrity in all causal analyses.
• Develop predictive and inferential models using regression, classification, time series forecasting, and other statistical approaches.
• Build reusable experimentation and causal inference frameworks that can be applied across different business areas.
• Create Python/R libraries and analytical packages that improve productivity and consistency.
• Automate analytical workflows and prepare production-ready data science solutions.
• Work with stakeholders to identify optimization opportunities through experiments and advanced analytics.
• Convert statistical results into clear, actionable recommendations for business leaders.
• Present insights to senior and executive stakeholders in a simple, compelling way.
• Support strategic decisions through data-driven storytelling.
• Guide junior data scientists and analysts on statistical methods, experimentation practices, and scalable solution design.
• Encourage innovation, quality, experimentation, and continuous improvement across teams.
• Lead cross-functional work involving analytics, engineering, and business functions.

Required qualifications

• A bachelor’s degree in Statistics, Economics, Computer Science, Engineering, Mathematics, Physics, or another quantitative discipline.
• At least 7 years of experience with a strong emphasis on experimentation, statistical modeling, and causal inference.
• Strong hands-on knowledge of regression analysis, classification, time series forecasting, experimental design, and statistical inference.
• Deep expertise in propensity score methods, synthetic controls, difference-in-differences, instrumental variables, doubly robust estimation, meta learners, and uplift modeling.
• Solid experience in A/B test design, geo experiments, power analysis, sample size estimation, minimum detectable effect, multiple testing corrections, false discovery rate control, Bayesian statistics, and frequentist statistics.
• Advanced proficiency in Python and R.
• Experience working with scikit-learn, LightGBM, and statistical analysis package development.
• Excellent written and verbal communication skills.
• Ability to explain complex analytical ideas to both technical and non-technical audiences.
• Experience presenting recommendations to senior executives.
• Strong stakeholder management and cross-functional collaboration skills.
• Ability to independently drive analytical projects from idea generation through implementation.

Preferred background

• A master’s degree in Computer Science, Statistics, Mathematics, or another quantitative field with at least 5 years of relevant experience.
• Alternatively, a PhD in a quantitative discipline with at least 3 years of relevant experience in experimentation or causal inference.
• Exposure to ETL development, data extraction, data transformation, data integration, and data quality management.
• Experience with CI/CD pipelines, production deployment, model monitoring, experiment monitoring, and automated reporting.
• Hands-on familiarity with Databricks, Jupyter Notebook, Snowflake, and GitHub.
• Prior work supporting subscription-based business models is a strong plus.
• Understanding of customer analytics, market trends, and consumer behavior is desirable.
• Ability to connect analysis with business strategy and commercial outcomes.
• Experience leading cross-functional projects and managing stakeholders and timelines in fast-moving environments.
• Track record of mentoring data science teams and encouraging strong analytical and engineering practices.

Additional information

Location: Glendale, CA. This is a full-time onsite role. The posting does not specify salary, stipend, number of openings, start date, or application deadline.

Education options mentioned

The role explicitly welcomes candidates with a bachelor’s degree in a quantitative field and also notes preferred master’s and PhD pathways with relevant experience.