Senior Applied Scientist, AWS Central Economics and Science

Amazon • Seattle, WA

4d • Hybrid

About The Position

AWS is one of Amazon’s largest and fastest growing businesses, serving millions
of customers in more than 190 countries. We use cloud computing to reshape the
way global enterprises use information technology. We are looking for
entrepreneurial, analytical, creative, flexible leaders to help us redefine the
information technology industry. If you want to join a fast-paced, innovative
team that is making history, this is the place for you.

AWS Central Economics & Science (ACES) drives best practices for objectively
applying economics and science in decision making across AWS. The team
collaborates with AWS science and business teams to identify, frame, and analyze
complex and ambiguous problems of the highest priority. Through data-driven
insights and modeling, ACES supports strategic decision-making across the AWS
global organization, including sales operations and business performance
optimization.

The ACES Sales Channels team is hiring an Applied Scientist (Senior or below) to
advance our mission of providing rigorous, causal-inference-driven
recommendations for AWS sales optimization. This role will focus on building ML
systems with a causal modeling foundation, designing seller incentive
mechanisms, and developing intervention strategies across the entire sales
motion.

The ACES Sales Channels team works on understanding and optimizing AWS's sales
channels, both direct (generalist and specialist sellers) and indirect (partners
and Marketplace). Our work falls into three core areas: developing rigorous
causal measurement and modeling frameworks using cutting-edge economics and
statistical methods; designing programs and incentives to improve customer and
business outcomes; and building ML-based recommendation systems for sellers,
partners, and other AWS stakeholders.

Why AWS?
Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted
cloud platform. We pioneered cloud computing and never stopped innovating —
that’s why customers from the most successful startups to Global 500 companies
trust our robust suite of products and services to power their businesses.

Inclusive Team Culture
Here at AWS, it’s in our nature to learn and be curious. Our employee-led
affinity groups foster a culture of inclusion that empower us to be proud of our
differences. Ongoing events and learning experiences, including our
Conversations on Race and Ethnicity (CORE) and AmazeCon conferences, inspire us
to never stop embracing our uniqueness.

Mentorship & Career Growth
We’re continuously raising our performance bar as we strive to become Earth’s
Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and
other career-advancing resources here to help you develop into a better-rounded
professional.

Work/Life Balance
We value work-life harmony. Achieving success at work should never come at the
expense of sacrifices at home, which is why we strive for flexibility as part of
our working culture. When we feel supported in the workplace and at home,
there’s nothing we can’t achieve in the cloud.

Requirements

• 3+ years of building machine learning models for business application experience
• PhD, or Master's degree
• Experience programming in Java, C++, Python or related language
• Experience with neural deep learning methods and machine learning

Nice To Haves

• Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
• Experience with large scale distributed systems such as Hadoop, Spark etc.

Responsibilities

• Causal ML System Development: Build and deploy machine learning models that emphasize causal inference, ensuring recommendations are grounded in valid interventions
• Incentive Design: Define and model incentives that drive desirable behaviors across AWS sales channels, partner programs, and reseller ecosystems
• Stakeholder Collaboration: Work with business stakeholders to understand requirements, validate approaches, and ensure practical applicability of scientific solutions
• Scientific Rigor: Promote findings at internal conferences and contribute to the team's reputation for methodological excellence

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

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: Ph.D. or professional degree