Principal Data Scientist, AWS Analytics Engineering (AAE) (Seattle)
Description Do you want to define the multi-year science vision that transforms how millions of customers experience AWS products? Do you want to influence the AWS investment in Cloud and AI technology and see how your recommendations influencing AWS VP level decisions and driving the growth of AWS business? Do you want to push the boundaries of data science (e.g. statistical modeling, causal inference, econometrics, product growth analytics, and forecasting models) and democratize how AWS senior leaders access analytics insights using agentic analytics system? The AWS Analytics Engineering is at the forefront of leveraging cutting-edge AI/ML technology and infrastructure to redefine how AWS product leaders and teams interact with and derive insights from their product and customer data. Our vision is to use data science methods to enable AWS product teams and business leaders to drive product and revenue growth and create personalized, optimized, and simplified product experiences to delight our customers. We are looking for a customer-focused Principal Data Scientist to lead and define the science strategy across AWS services. In this role, you will set the technical direction for ML-driven product analytics across AWS Compute (EC2), GenAI & Agents, Database & Analytics, and Storage (S3) organizations. You will partner directly with GMs, VPs, and senior product leaders to translate complex business challenges into innovative scientific solutions that directly influence AWS's top line and bottom line. You will analyze underlying product growth insights, understand product growth drivers, and anticipate business risks that need to be surfaced to leadership. Key job responsibilities Define and drive the multi-year science vision and data science roadmap for AWS product growth analytics across AWS Compute, Database & Analytics, Storage, AI/ML, and other organizations Attend AWS WBR to answer critical and timely business and analytics questions to drive clarify on AWS’ product growth strategy Influence senior leaders across multiple organizations by building mental models on AWS growth and anticipate growth risks that should be mitigated Serve as the technical thought leader and strategic advisor to senior AWS leaders (GM/VP level), translating business objectives into high-impact scientific decisions and identify opportunities that drives overall AWS product and revenue growth Establish best practices for decision science, including econometrics, statistical modeling, and causal methods Invent, operationalize, and scale novel analytical frameworks and metrics that enable data-driven product growth and executive decision-making Mentor junior decision scientists, setting the bar for technical quality through code reviews, design reviews, and hands‑on guidance Communicate findings, conclusions, and strategic recommendations to both technical and non-technical executive audiences through effective verbal and written communication Identify and champion new science opportunities that expand AAE’s impact across AWS, building the case for investment and driving adoption A day in the life As a Principal Data Scientist in AAE org, you will shape the science strategy that underpins product decisions across multiple AWS organizations. You'll spend your time partnering with VPs and GMs to identify the highest-leverage data science opportunities, architecting novel ML solutions to complex product challenges, and mentoring scientists across the team. You'll drive alignment across cross-functional stakeholders, ensure scientific rigor in our most critical initiatives, and communicate insights that directly influence AWS product roadmaps and growth strategy. You'll balance long-term vision-setting with hands‑on technical leadership, diving deep into model architectures and data pipelines when needed. About The Team We are a team of scientists and engineers supporting AWS product leaders to make high-impact decisions through sophisticated analytical frameworks, trusted data science methods, and scalable ML products. We come from diverse backgrounds in statistics, computer science, engineering, and business analytics. We specialize in the full end-to-end ML development process, including data ingestion, ETL, model development, and model deployment in production. We support data science needs across AWS EC2, Database & Analytics, and S3 teams using deep learning, graph neural networks, forecasting, reinforcement learning, causal inference, and more. High Impact Projects: We work on high-impact, high-visibility projects that directly influence AWS product roadmaps and senior leaders' decisions. Supportive Team Environment: We are proud of our supportive and inclusive team culture, we have each other's back during ups and downs. Work-Life Balance: We believe 80% of value comes from 20% of work, so we always prioritize our backlog ruthlessly based on business value. Learning Opportunity: Extensive opportunities to understand AWS business and leverage state-of-the-art AI/ML and cloud technology. Basic Qualifications Bachelor's degree in engineering, statistics, computer science, mathematics, or a related quantitative field 10 years of data scientist or similar role involving data extraction, analysis, statistical modeling and communication experience Competency in data querying languages (e.g., SQL) and scripting languages (e.g., Python, R) Experience with advanced machine learning techniques including deep learning, causal inference, and experimentation systems Experience leading large-scale technical or scientific programs with a proven record of thought leadership and successful delivery Track record of influencing senior leadership (Director/VP level) through data-driven insights and strategic recommendations Preferred Qualifications PhD or Master's degree in Computer Science, Statistics, Machine Learning, Economics, Operations Research, or a related quantitative field Knowledge of AWS technology stack or Certification Experience with Gen AI, large language models, Baysian, Econometrics, or reinforcement learning in applied settings Demonstrated ability to build and mentor high-performing science teams Experience establishing measurement frameworks and experimentation systems at scale Excellent communication skills with non-technical executive audiences Publication experience at top-tier conferences or journals 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 - 189,400.00 - 256,200.00 USD annually J-18808-Ljbffr
