Minimum qualifications:

• Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field.
  
• 3 years of work experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or a PhD degree.
  

Preferred qualifications:

• 5 years of work experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or a PhD degree.
  
• Experience with statistical data analysis such as linear models, multivariate analysis, stochastic models, sampling methods.
• Experience articulating and translating business questions and using statistical techniques to arrive at an answer using available data.
• Applied experience with machine learning on large datasets.
• Understand potential outcomes framework and have familiarity with causal inference methods such as split-testing, instrumental variables, fixed effects regression, panel data models, regression discontinuity, matching estimators.

About the job

In this role, you will be quantitatively trained with expertise in quantitative methodologies, and with a solid understanding of statistics and causal inference methods. You will have an interest in understanding consumer behavior, advertising and privacy with a passion for business problems, and an interest in combining data and strategy will grow in our environment. You will be able to leverage their training and their skills to leverage data and technology to make business decisions. You will be comfortable working cross-functionally and grow in a changing, science-driven organization.

Responsibilities

• Help suggest, support, and shape new data-driven advertising measurement products, with a primary focus on lift and incrementality measurement.
• Design and analyze large-scale experiments—including both user-level and geo-based randomized controlled trials—to evaluate advertising effectiveness and establish causal impact.
• Develop and implement advanced quantitative methods by combining causal inference, statistical modeling, and machine learning to solve complex analysis problems related to conversion lift.
• Conduct end-to-end analyses of large, complex data sets, building scalable and iterative analysis pipelines to provide incrementality insights to our partners at scale.
• Work cross-functionally to provide business recommendations, translating complex experimental findings into clear, actionable insights for engineering, product leaders, and key stakeholders.