Job Description

DESCRIPTION

Over 2 million Sellers in 10 countries ship billions of products for sale on the Amazon Marketplace. To meet our sellers needs, we are constantly innovating and building on new ideas. Fulfillment by Amazon (FBA) Inbound data science team partners with product team to optimize inbound supply chain by influencing right tradeoffs among cost, speed and network capacity utilization.

FBA Inbound data science team is looking for an Applied Scientist to build and productionize cutting edge optimization and machine learning solutions, leveraging 100s of Terabytes of big data, to influence optimal placement of billions of units flowing through the global supply chain network. These solutions operate at millisecond latencies at Amazon scale, to optimize supply chain placement decisions across dozens of global fulfilment centers.

Your responsibilities include:
Collaborate with scientists, engineers and product managers to identify the highest-impact science projects to solve customer problems.
Design, implement, test, deploy and maintain innovative science solutions to accelerate the business impact.
Evaluate the proposed solutions via offline simulations as well as online A/B tests in production.
Publish and present your work at internal and external scientific venues in the fields of ML/Optimization/Recommender systems.
Work with peer scientists to review the science proposals and provide feedback.

Your benefits include:
Working on a high-impact, high-visibility product, with your work improving the experience of millions of customers.
The opportunity to use (and innovate) state-of-the-art ML methods to solve real-world problems.
Excellent opportunities, and ample support, for career growth, development, and mentorship.
Competitive compensation, including relocation support.
To help describe some of our challenges, we have created a short video about Supply Chain Optimization at Amazon - http://bit.ly/amazon-scot

Amazon is an Equal Opportunity-Affirmative Action Employer Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation / Age
BASIC QUALIFICATIONS

Master's degree in Computer Science, Operations Research, Engineering, Mathematics, or Statistics or a related field.
3+ years of hands-on experience in machine leaning, optimization and/or recommender systems.
Prior work experience as an applied scientist or a data scientist at a consumer product company.
At least one publication, as first author, in a leading conference or journal related to machine learning.
Strong coding and problem-solving skills in at least one programming language such as R, Python, Java, C++, etc.
Working knowledge of web-scale data processing (e.g., Hadoop, Spark).
Sound theoretical understanding of broad machine learning concepts, with deep and demonstrable expertise in at least one topic of specialization.
Good written communication skills and ability to convey mathematical concepts to non-technical audience.

PREFERRED QUALIFICATIONS

A PhD in a quantitative field (Computer Science, Mathematics, Machine Learning, AI, Statistics, or equivalent)
5+ years of hands-on experience in machine leaning, optimization and/or recommender systems.
Prior work experience as an applied scientist or a data scientist at a consumer product company.
At least one publication, as first author, in a leading conference or journal related to machine learning.
Strong coding and problem-solving skills in at least one programming language such as R, Python, Java, C++, etc.
Working knowledge of web-scale data processing (e.g., Hadoop, Spark).
Sound theoretical understanding of broad machine learning concepts, with deep and demonstrable expertise in at least one topic of specialization.
Good written communication skills and ability to convey mathematical concepts to non-technical audience.
Demonstrated industry leadership in your area of specialization.