In this role, you will have the opportunity to:
Leverage economic methods like modeling and impact estimation to address key issues using large, real-world datasets;
Partner with PMs and BAs to identify data and define metrics evaluating business initiatives and making recommendations;
Execute from idea to implementation as an integral part of cross-functional teams;
Thrive in a highly complex and fast pacing environment.
Participate in research activities, including publishing papers, attending conferences, and collaborating with academic institutions to advance the state-of-the-art in relevant fields.
Key job responsibilities
In this role you will:
Build causal inference modeling to evaluate the effects of policy changes, such as fee adjustments and new fee structures, on seller behaviors and business metrics;
Analyze how sellers and key outcomes are impacted by variations in growth strategies;
Design experiments to measure pilot programs and support scaling-up effort;
Synthesize learnings from past policy changes into critical insight to help the business develop new strategies and make science-based decisions.
Work closely with a senior science advisor, collaborate with other scientists and engineers, and be part of Amazon’s vibrant and diverse global science community.
Publish your innovation in top-tier academic venues and hone your presentation skills.
Be inspired by challenges and opportunities to invent cutting-edge techniques in your area(s) of expertise.
About the team
The Fee Science Team works on science, economic, machine learning, and data engineering projects for Selling Partner Services and third-party fees. Our projects include causal modeling and impact of structural changes, models that support fee integrity, business analytics, and a comprehensive set of data sources and pipelines.
BASIC QUALIFICATIONS
Basic qualifications include:
- PhD in a relevant field, received within 2 years of starting the program
- Proven publication record in Operation Research, Management Science, Statistics, Machine Learning, Computer Science, Econ, or other related technical fields
- Experience in data science and quantitative research
- Proficiency in technologies relevant to the subfield
PREFERRED QUALIFICATIONS
Postdocs demonstrate the following preferred job qualifications:
- Ability to independently deliver results in a fast-paced environment
- Publications at top-tier, peer-reviewed conferences and/or journals
- Exceptional verbal and written communication skills
- Expert knowledge in modeling and performance, operationalization, and scalability of scientific techniques and establishing decision strategies
Required application materials:
- CV, which lists all peer-reviewed publications and conferences,
- Research statement that outlines your research achievements and future research interests, and
- A journal article or book chapter that demonstrates your domain expertise.
Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.