Data Scientist, Advertising, AMPI Measurement
Job
Amazon.com, Inc.
Seattle, WA (In Person)
Full-Time
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Job Description
Description Amazon is investing heavily in building a world-class advertising business, and we are responsible for defining and delivering a collection of advertising tools and products that drive discovery and Advertiser success. Our products are strategically important to our Retail and Marketplace businesses, driving long-term growth. We deliver billions of ad impressions and millions of clicks daily and are breaking fresh ground to create world-class products. We are highly motivated, collaborative, and fun-loving with an entrepreneurial spirit and bias for action. The Marketing Effectiveness & Attribution Science team develops causal inference and machine learning systems to measure the impact of marketing programs across Amazon's advertising ecosystem. We build production-grade attribution models that help business teams understand what's working, optimize resource allocation, and drive advertiser growth. Our work sits at the intersection of econometrics, scalable ML systems, and high-stakes business decisions. As a Data Scientist on this team, you will own end-to-end modeling pipelines — from problem formulation and experimental design to model development, productionization, and stakeholder communication.
Major responsibilities include:
Translate /Interpret:
Partner with cross-functional teams to translate business questions into rigorous causal inference problems Design observational studies and quasi-experiments to measure marketing effectiveness when traditional A/B tests are infeasible Work with data engineering to instrument new data pipelines when existing data cannot answer the causal question Measure / Quantify /Expand:
Own and evolve production attribution models across multiple marketing channels Build and maintain causal inference pipelines using methods such as Difference-in-Differences, Synthetic Control, Double Machine Learning, and Media Mix Models Develop scalable PySpark and Python codebases that process large-scale event data Continuously improve model accuracy through feature engineering, heterogeneity analysis, and sensitivity testing Explore /Enlighten:
Investigate anomalies in model outputs and deep-dive to identify root causes Develop automated data quality checks and model diagnostics Research and prototype next-generation measurement methods Make Decisions /Recommendations:
Present findings to senior leadership with clear recommendations Build dashboards and self-service tools that enable stakeholders to explore results independently Write production-quality Python code for data analysis, model training, and result publishing Basic Qualifications- 3+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience
- 2+ years of data scientist experience
- 3+ years of machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance experience
- Bachelor's degree
- Experience applying theoretical models in an applied environment Preferred Qualifications
- Experience in Python, Perl, or another scripting language
- Experience in a ML or data scientist role with a large technology company
- Knowledge of relevant statistical measures such as confidence intervals, significance of error measurements, development and evaluation data sets, etc.
USA, WA, SEATTLE
- 136,000.00
- 184,000.
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