Applied Scientist II, Seller Fee Science
Amazon · Bangalore, India · Applied Science
About this role
Amazon is hiring a mid-level Applied Scientist in the machine learning function based in Bangalore, India. The posting calls out experience with Python, Java, NLP, Deep Learning. Listed education preference: a bachelor's degree or equivalent.
- Role
- Applied Scientist
- Function
- machine learning
- Level
- mid
- Track
- Individual contributor
- Employment
- Full-time
- Location
- Bangalore, India
- Education
- Bachelor's degree
- Department
- Applied Science
- Posted
- Mar 19, 2026
More roles at Amazon
Job description
from Amazon careersAmazon’s third-party marketplace is a multibillion-dollar global ecosystem, connecting customers and sellers across the world through millions of transactions annually. The Seller Fee Science Team integrates economic modeling, machine learning, and artificial intelligence to guide business fee strategy, ensure fees are accurately computed for millions of products, and improves the seller experience with AI tools that support any fee related contact (understanding, audit, and dispute). We build the scientific foundation that empowers sellers to grow their businesses with clarity and confidence. Our team brings together world-class economists, physicists, mathematicians, and computer scientists to tackle diverse challenging problems that require theoretical rigor and deliver real-world impact. For example, precision measurement of difficult to measure products, large-scale simulation of sales, inventory, and policy changes, as well as leveraging natural language understanding and automated reasoning to interpret policy, generate code, resolve disputes, audit fees, and respond to sellers at meaninful scale. As an applied scientist on our team, this role will focus on the application of machine learning and artificial intelligence to predict and reconcile measurement of products globally. This blends together statistical modeling, application of NLP, image processing, classical machine learning, cost-benefit analysis, causal modeling, and optimization. Your work will shape not…