Principal Applied Scientist, Amazon Stores Economics & Science (SEAS)
Amazon · Palo Alto, CA · Applied Science
About this role
Amazon is hiring a principal-level Applied Scientist in the machine learning function based in Palo Alto, CA. The posting calls out experience with Python, Java, R, Spark and roughly 10+ years of relevant work. Listed education preference: a master's degree or equivalent. Compensation is listed at $228,700–$309,400 per year.
- Role
- Applied Scientist
- Function
- machine learning
- Level
- principal
- Track
- Tech leadership
- Employment
- Full-time
- Location
- Palo Alto, CA
- Experience
- 10+ years
- Education
- Master's degree
- Department
- Applied Science
- Posted
- Oct 8, 2024
More roles at Amazon
Job description
from Amazon careersStores Economics and Science (SEAS) is an interdisciplinary science and engineering team in Amazon's Stores organization with a peak-jumping mission: we apply expertise in science and engineering to move from local to global optima in methods, models, and software. We pursue this mission by leveraging frontier science; collaborating with partner teams; and learning from the tools, experience, and perspective of others. We scale by solving problems, first in the small to prove concepts, and then in the large by building scalable solutions. We also help other teams within Amazon scale by hiring and developing the best and embedding them in other business units. In 2024, we are focused on economics and science in areas related to (1) improving delivery speed and lowering cost-to-serve, (2) seller fees and incentives, and (3) emerging machine learning using LLMs. We also have some ongoing and highly leveraged collaborations that help partner teams inside Amazon short-circuit months of R D or otherwise look around corners. We are looking for a seasoned Applied Science leader to build and deliver cutting-edge science and engineering solutions to improve our Stores business. In this role, you will lead a team of scientists and engineers with backgrounds in machine learning,…