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 Applied 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.

An ideal candidate will be an expert in the areas of machine learning, operations research, and statistics. With expertise in applying theoretical models in an applied environment relying heavily on the latest advances in machine learning, optimization, stochastic modeling, and engineering. The candidate will be expected to work on numerous aspects, such as feature engineering, modeling, probabilistic modeling, hyper-parameter tuning, scalable inference methods and latent variable models. 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

- Master's degree in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field
- Experience building machine learning models or developing algorithms for business application
- Experience in solving business problems through machine learning, data mining and statistical algorithms
- Experience in state-of-the-art deep learning models architecture design and deep learning training and optimization and model pruning
- Experience with theory and practice of design of experiments and statistical analysis of results
- Experience programming in Java, C++, Python or related language
- Experience applying theoretical models in an applied environment

Preferred Qualifications

- Experience implementing algorithms using both toolkits and self-developed code
- Have publications at top-tier peer-reviewed conferences or journals

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