Data Scientist, Seller Fulfillment Services (SFS)

Data Scientist, Seller Fulfillment Services (SFS)

If you have ever bought or sold anything on Amazon, you have touched Amazon Marketplace. Amazon’s Marketplace business is one of the largest in the world. We are now in 23 countries. We are growing fast, with customers in many more countries. Amazon’s platform is the engine that powers Amazon’s Marketplace businesses, and Sellers rely on this platform and our support to start selling on Amazon and to grow their business. Amazon Marketplace enables millions of Sellers worldwide to list hundreds of millions of products and manage orders for inventory across dozens of different categories and languages. While working with millions of Sellers worldwide, we constantly strive to improve the selection for Customers and the capabilities of our platform for Sellers.

The Seller Fulfillment Services (SFS) team is looking for a motivated and innovative Data Scientist with strong analytical skills and practical experience to join our science team. As a key member of the SFS science team, you will provide expertise that helps accelerate the business. You will build science solutions that will help us to provide our customers with the largest selection of merchants at the lowest, and the most reliable delivery service regardless of the seller. You will research, design and improve on the models that will impact Amazon’s customer directly. You will be working in a highly collaborative environment partnering with various science, product management, engineering, operations, finance, business intelligence and analytics teams to develop science models to solve business problems. You will need to understand the business requirements and translate them into complex analytical outputs. You will design tests to explain performance of the models from impact on customer and cost perspective. You will create ML models to capture features impacting performance. You should be comfortable building prototypes, testing and improving them given the feedback from the real time data. You should be able to present your model and findings to a various range of stakeholders.

Looking for candidate with expertise in the areas of machine learning. The candidate will be expected to work on numerous aspects, such as feature engineering, modeling, and hyper-parameter tuning. Challenges will involve dealing with very large data sets and requirements on throughput.

Key job responsibilities
Design, implement, test, deploy, and maintain innovative science solutions to accelerate our business.
Create experiments and prototype implementations of new learning algorithms and prediction techniques.
Collaborate with scientists, engineers, product managers, and stakeholders to design and implement software solutions for science problems.
Use best practices to ensure a high standard of quality for all of the team deliverables

Basic Qualifications

- 2+ years of data scientist experience
- 3+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience
- 3+ years of machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance experience
- 1+ years of guiding and coaching a group of researchers experience
- 1+ years of working with or evaluating AI systems experience
- 1+ years of creating or contributing to mathematical textbooks, research papers, or educational content experience
- Master's degree in Science, Technology, Engineering, or Mathematics (STEM), or experience working in Science, Technology, Engineering, or Mathematics (STEM)
- Experience applying theoretical models in an applied environment

Preferred Qualifications

- Ph.D. in Science, Technology, Engineering, or Mathematics (STEM)
- Knowledge of machine learning concepts and their application to reasoning and problem-solving
- Experience in Python, Perl, or another scripting language
- Experience in a ML or data scientist role with a large technology company
- Experience in defining and creating benchmarks for assessing GenAI model performance
- Experience working on multi-team, cross-disciplinary projects
- Experience applying quantitative analysis to solve business problems and making data-driven business decisions
- Experience effectively communicating complex concepts through written and verbal communication

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Experience Level

Mid Level