Data Scientist, Advertising, AMPI Measurement

Amazon • Seattle, WA

2d • Onsite

About The Position

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.

Requirements

• 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

Nice To Haves

• 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.

Responsibilities

• 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
• 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
• 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
• 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

Benefits

• 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
• parental leave
• sign-on payments
• restricted stock units (RSUs)

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level