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Applied Scientist, Advertising, AMPI Measurement
Seattle, Washington, USAPosted 2 days ago
Full-timeonsite
Job 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 & Measurement 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 measurement models and the calibration system that serves as the measurement truth layer — continuously validating model outputs against RCTs and certifying them for high-stakes business and finance decisions. As our signals increasingly feed automated, machine-speed consumers, we are also transforming how we operate: building AI-assisted pipelines and agents that automate onboarding, backfills, diagnostics, and reporting so scientists can focus on judgment and method. Our work sits at the intersection of econometrics, scalable and reliable ML systems, calibration, and high-stakes business decisions.
As an Applied Scientist on this team, you will own end-to-end modeling and production pipelines — from problem formulation and experimental design through model development, productionization, calibration, and stakeholder communication — increasingly augmented by AI tooling and agents.
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, with the reliability, latency, and reproducibility that automated downstream consumers depend on
Build and maintain causal inference pipelines using methods such as Difference-in-Differences, Synthetic Control, Double Machine Learning, and Media Mix Models
Develop and maintain calibration systems that benchmark model outputs against RCTs — owning the measurement truth layer
Write scalable, modular, SDE-standard PySpark/Python codebases (CI/CD, test isolation, structured logging) that process large-scale event data and deploy to production with confidence
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
Research and prototype next-generation measurement methods and apply AI/LLM-based tooling to accelerate the science development lifecycle
Make Decisions / Recommendations
Present findings to senior leadership with clear recommendations
Build dashboards, agent-consumable APIs, and self-service tools that let stakeholders (and downstream systems) explore results independently
Write production-quality Python for data analysis, model training, calibration, and result publishing
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
- 3+ years of building models for business application experience
- PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
- Experience programming in Java, C++, Python or related language
- Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
- Experience in professional software development
- Experience in designing experiments and statistical analysis of results
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
USA, WA, SEATTLE - 142,800.00 - 193,200.00 USD annually
The Marketing Effectiveness & Measurement 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 measurement models and the calibration system that serves as the measurement truth layer — continuously validating model outputs against RCTs and certifying them for high-stakes business and finance decisions. As our signals increasingly feed automated, machine-speed consumers, we are also transforming how we operate: building AI-assisted pipelines and agents that automate onboarding, backfills, diagnostics, and reporting so scientists can focus on judgment and method. Our work sits at the intersection of econometrics, scalable and reliable ML systems, calibration, and high-stakes business decisions.
As an Applied Scientist on this team, you will own end-to-end modeling and production pipelines — from problem formulation and experimental design through model development, productionization, calibration, and stakeholder communication — increasingly augmented by AI tooling and agents.
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, with the reliability, latency, and reproducibility that automated downstream consumers depend on
Build and maintain causal inference pipelines using methods such as Difference-in-Differences, Synthetic Control, Double Machine Learning, and Media Mix Models
Develop and maintain calibration systems that benchmark model outputs against RCTs — owning the measurement truth layer
Write scalable, modular, SDE-standard PySpark/Python codebases (CI/CD, test isolation, structured logging) that process large-scale event data and deploy to production with confidence
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
Research and prototype next-generation measurement methods and apply AI/LLM-based tooling to accelerate the science development lifecycle
Make Decisions / Recommendations
Present findings to senior leadership with clear recommendations
Build dashboards, agent-consumable APIs, and self-service tools that let stakeholders (and downstream systems) explore results independently
Write production-quality Python for data analysis, model training, calibration, and result publishing
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
- 3+ years of building models for business application experience
- PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
- Experience programming in Java, C++, Python or related language
- Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
- Experience in professional software development
- Experience in designing experiments and statistical analysis of results
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.
USA, WA, SEATTLE - 142,800.00 - 193,200.00 USD annually