Key job responsibilities
1. Design and implement scalable, statistically rigorous experiments (A/B, geo, holdout, quasi-experiments) to measure marketing incrementality across channels.
2. Perform power analysis and minimum detectable effect (MDE) calculations to determine experiment sample sizes, durations, and design trade-offs before launch.
3. Build causal and treatment-effect models that produce conversion and valuation signals consumed by downstream bidding and budgeting systems.
4. Building the ETL, metric definitions, and datasets that make results scalable, extensible, and repeatable rather than one-off analyses.
5. Develop measurement frameworks that quantify the true, platform-independent contribution of marketing over time, and build the dashboards and reporting that keep those metrics visible to the business.
6. Apply statistical, mathematical, and machine learning techniques to solve ambiguous business problems where the right approach isn't obvious.
7. Analyze experiment results for validity — inspecting distributions, checking for sample ratio mismatch, exploring covariate balance, and tracking down the source of anomalies.
8. Communicate experiment design, results, and trade-offs clearly to business and leadership audiences, including inputs into business reviews, and influence decisions and technical direction across teams.
9. Establish scalable, repeatable processes and best practices for experiment design, data modeling, and analysis.
4+ years of data scientist experience
4+ years of machine learning, statistical modeling, data mining, and analytics techniques experience
4+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience
1+ years of working with or evaluating AI systems experience
Experience applying theoretical models in an applied environment
Master's degree in Science, Technology, Engineering, or Mathematics (STEM)
Knowledge of machine learning concepts and their application to reasoning and problem-solving
Experience in a ML or data scientist role with a large technology company
Experience in defining and creating benchmarks for assessing GenAI model performance
Experience working on multi-team, cross-disciplinary projects
Experience applying quantitative analysis to solve business problems and making data-driven business decisions
Experience effectively communicating complex concepts through written and verbal communication
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