Principal Applied Scientist, Optimal Sourcing Systems

2 weeks ago


New York, United States Amazon.com Services LLC Full time
Are you seeking an environment where you can drive innovation? Do you want to be at the forefront of solving the toughest real-world supply chain problems? Do you want to play a key role in the future of Amazon's Stores business? Come and join us

The Supply Chain Optimization Technologies (SCOT) group is seeking a Principal Applied Scientist to join our Optimal Sourcing Systems team. The Optimal Sourcing Systems (OSS) owns the optimization of inventory sourcing and the orchestration of inbound flows from vendors worldwide. We source inventory from thousands of vendors for millions of products globally while orchestrating the inbound flow for billions of units. Our goals are to increase access to supply for speed and placement, improve supply chain-driven vendor experience, and reduce end-to-end supply chain costs, all in service of maximizing Long-Term Free Cash Flow (LTFCF) for Amazon.

Your tech solution will have large impacts to the physical supply chain of Amazon, and play a key role in improving Amazon consumer business’s long term profitability. You will work on some of the most difficult problems in the industry with some of the best product managers, scientists, and software engineers in the industry.

Key job responsibilities
Key job responsibilities include:

- Design, implement and evaluate models, agents and software prototypes
- Technical leadership for a group of highly motivated and talented scientists
- Engage key business stakeholders and scientists to surface opportunities for improvement and identify business requirements
- Work closely with partner teams to develop solutions to ambiguous business problems and integrate novel methodology into our team and business
- 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
- Raise the bar of scientific research by innovating and publishing

A day in the life
In this role, you will have opportunity to work with partners and stakeholders from Amazon’s retail, seller and operation departments worldwide. You will understand their challenges and pain points, and help develop solutions that improve how Amazon source inventory and enable world class vendor experiences. To implement your solutions, you will work closely with our product and engineering teams. Your work will have high visibility and impacts to Amazon’s business operation.

About the team
Supply Chain Optimization Technologies (SCOT) owns Amazon's global inventory planning systems. We decide what, when, where, and how much we should buy to meet Amazon's business goals and to make our customers happy. We decide how to place and move inventory within Amazon's fulfillment network. We do this for hundreds of millions of items and hundreds of product lines worth billions of dollars worldwide. Check our website if you are curious to learn more about the breadth of problems we tackle:

We are open to hiring candidates to work out of one of the following locations:

Bellevue, WA, USA | New York, NY, USA

BASIC QUALIFICATIONS

- PhD in one of the following disciplines: Computer Science, Machine Learning, Statistics, Operations Research or a related quantitative field
- 7+ years of relevant, broad research experience after PhD
- Have production coding experience in one of the object-oriented programming languages such as Java, Python, C++, etc.
- Strong algorithm development experience
- Depth and breadth in state-of-the-art machine learning and Optimization technologies
- 7+ years of hands-on experience in building machine learning or optimization models in business environment
- Strong prior experience with mentorship and/or management of senior scientists and engineers

PREFERRED QUALIFICATIONS

- Knowledge of the latest trends in related areas in Machine Learning
- Expertise in Machine Learning or Reinforcement Learning
- Proven track record of innovation in creating novel algorithms and advancing the state of the art



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