Senior Applied Science Manager, AWS Analytics Engineering (AAE)

About the role

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 GenAI technology and see the impact of your leadership moving the needle on billions of dollars of AWS business? Do you want to lead a cross-functional team that impacts multiple organizations (product, sales, marketing, finance) in AWS? Do you want to push the boundaries of AI/ML technology (e.g. multi-agent analytics system, agentic knowledge representation and management, graph neural networks, reinforcement learning, causal inference, optimization, and LLM-based forecasting models) to build scalable ML products that help AWS grow and delight our customers?

The AWS Analytics Engineering (AAE) 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 data. We are a cross-functional org from decision science, ML products, data platform, and agentic analytics system. Our vision is to use artificial intelligence and machine learning to enable AWS product teams, product, and go-to-market leaders to drive product growth and create personalized, optimized, and simplified product experiences to delight our customers. We shape AWS product features (e.g. Console, Spot and Autoscaling), influence GTM efficiencies with customer propensity models, democratize data and insights access through multi-agent systems, and influence AWS leaders’ product strategy.

Responsibilities

• Define and drive the multi-year science, ML product, and software engineering vision and roadmap for ML-powered product analytics across AWS Products, Marketing, and Sales organization
• Build, lead, and develop a high-performing team of technical managers, applied scientists, and software engineers, including hiring top talent, managing performance, and growing careers through mentorship and promotion readiness
• Partner with senior AWS leaders (GM/VP level) to identify strategic, data-driven opportunities and translate business objectives into high-impact scientific initiatives
• Architect and guide enterprise-scale ML and agentic platform, including agentic systems, knowledge representation, big data platform, deep learning, graph neural networks, reinforcement learning, causal inference, and forecasting models that predict business outcomes and enhance customer experiences
• Manage cross-functional science and software engineering teams to build ML-driven products that are scalable and leading industry best practices at the AWS scale and speed
• Invent, operationalize, and scale novel analytical frameworks and metrics that enable data-driven product growth and executive decision-making
• Communicate findings, conclusions, and strategic recommendations to technical and non-technical business leaders across AWS
• Mentor scientists and engineers, establish best practices for experiment design and model evaluation, and review technical artifacts to ensure quality
• 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 senior applied science manager in the 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 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. You'll balance long-term vision-setting with hands-on technical leadership, diving deep into model architectures and data pipelines when needed while maintaining the strategic altitude to guide the team's direction. You will manage a cross-functional science and engineering team to build cutting-edge agentic systems and ML products that transform our product and customer experience.

About the team

We are a team of scientists and software 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 provide AI/ML services across decision science, ML products, multi-agent analytics systems, and data engineering platforms.

• 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.

Qualifications

Basic Qualifications

• 10+ years of building large-scale machine learning and AI solutions at Internet scale experience
• Master's degree in Computer Science (Machine Learning, AI, Statistics, or equivalent)
• Experience distilling informal customer requirements into problem definitions, dealing with ambiguity and competing objectives
• Experience hiring and leading experienced scientists as well as having a successful record of developing junior members from academia or industry to a successful career track

Preferred Qualifications

• 10+ years of practical work applying ML to solve complex problems for large-scale applications experience
• 5+ years of hands-on work in big data, machine learning, and predictive modeling experience
• 5+ years of people management experience
• PhD in Computer Science (Machine Learning, AI, Statistics, or equivalent)
• Experience in practical work applying ML to solve complex problems for large-scale applications
• Experience working with big data, machine learning, and predictive modeling

Pay

USA, WA, Seattle - $218,800.00 - $295,900.00 USD annually. 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.

Benefits

Amazon offers comprehensive benefits including:

• Health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance, and option for Supplemental life plans)
• Employee Assistance Program (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 amazon.jobs/en/benefits.