- MS or PhD in Artificial Intelligence, Computer Science (Machine Learning), Physics, Statistics, Applied Math, or Operations Research
- Experience with fast prototyping
- Proficiency with a scripting language such as R, Matlab, Python
- 3+ years of hands on experience working on applied machine learning projects.
- An attitude to continuously innovate and never satisfied with status quo
- Influence senior leadership teams on the value propositions and generate new product ideas.
- Mentor and guide junior members in the team.
- Work and collaborate effectively with tech teams, and launch to production.
- Contribute to Amazon's Intellectual Property through patents and/or external publications.
- Understand business context to decisions made within and across the organization
- Excellent verbal and written communication
The Supply Chain Optimization Technologies (SCOT) team is a machine learning powerhouse providing the brainpower behind the Amazon supply chain and using artificial intelligence to grow the most complex retail networks in the world.
The team is composed of scientists, engineers, and researchers who lead the industry in the development of innovative algorithms and optimization strategies to maximize the long term effectiveness of Amazon’s Retail business.
This team works to extend existing and build new algorithms and systems to achieve an ideal inventory position, maximizing in-stock selection on behalf of our customers and growing our Seller’s businesses. We own and operate production systems that decide the most optimal strategy to ship a customer order, simulation systems that allow internal customers experiment with what-if scenarios and analytical systems to help understand results and derive intelligence from the same.
The team in Austin is focused on using cutting edge science to improve customer outcomes and transform our logistics, along with machine learning, and scalable distributed software in the cloud that automates and optimizes shipments to customers under the uncertainty of demand, pricing and supply. When customers place orders, our systems use real time, large scale optimization techniques to optimally choose from where to ship and how to consolidate multiple orders so that customers get their shipments on time or faster with the lowest possible transportation costs.
As the Fulfillment Network Planning team, one of our core responsibilities is to leverage big data to identify key patterns of success and failure, identify the areas we need to focus on first, understand root cause(s) that triggered failure, and to build predictive models that will help fix the most impactful problems. The amount of data, the variables that come into play, and diversity of customers and locations make this role very challenging and also fun. It's great for those who love solving problems, especially when dealing with a lot of ambiguity and asking lots of smart questions that will lead to the discovery of universal concepts, and truly innovative ML solutions that continuously improve the customer delivery experience. Every member of the team needs to have independent thinking, a burning desire to learn, and the skills and experience needed to build science and technology so advanced that it will appear to generate “magical outcomes”.
As part of your daily work you will:
- Analyze and extract relevant information from large amounts of data to help automate and optimize key processes.
- Design, development and evaluation of highly innovative models for predictive learning.
- Think about customers and how to improve the customer delivery experience
- Use machine learning and analytical techniques to create scalable solutions for business problems.
- Work closely with data & software engineering teams to build model implementations and integrate successful models and algorithms in production systems at very large scale.
- Technically lead and mentor other scientists in team.
- Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation.
- Research and implement novel machine learning and statistical approaches.
To help describe some of our challenges, we 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.
- Knowledge of AWS Technologies
- Familiar with computer science fundamentals including object-oriented design, data structures, algorithm design, problem solving, and complexity analysis
- Proficiency with a production language such as C++ or Java
- A strong track record of innovating through machine learning and statistical algorithms and their applications.
- Strong demonstrated skills implementing and deploying large scale machine learning applications and tools.
- Ability to innovate in ML and a track record of publications.
- 5+ years of experience working on applied machine learning projects.
- Ability to work on a diverse team or with a diverse range of coworkers
Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us We believe passionately that employing a diverse workforce is central to our success and we make recruiting decisions based on your experience and skills. We welcome applications from all members of society irrespective of age, gender, disability, sexual orientation, race, religion or belief.