Amazon relies on its technology to deliver millions of packages every day to its customers – on time and with low cost. The Middle Mile Planning Research & Optimization Science team builds complex science models and solutions that work across our vendors, warehouses and carriers to optimize both time & cost of getting the packages delivered. Our models are state-of-the-art, make business decisions impacting billions of dollars in spend a year, and improve ordering and delivery experience for millions of online shoppers. That said, this remains a fast growing business and our journey has only started. Our mission is to build the most efficient and optimal transportation network on the planet, using our science and technology as our biggest advantage. We aim to leverage cutting edge technologies in optimization, operations research, and machine learning to grow our businesses.
As an Applied Scientist, you will research, develop, and deploy new models to support Amazon's sustainability goals and network planning initiatives. You will support Amazon's Climate Pledge - a commitment to be net-zero carbon across our business by 2040, 10 years ahead of the Paris Agreement. Your models will be used by Amazon worldwide, and you will have the opportunity to work with some of the leading scientists in the areas of operations research, integer programming, vehicle routing, and meta-heuristics.
Key job responsibilities
· Lead and partner with software engineering, product, and operations teams to drive modeling and technical design for complex business problems.
· Develop accurate and scalable optimization, mathematical programming, and heuristic methods to solve our hardest transportation problems.
· Lead complex modeling analyses to aid management in making key business decisions and set new policies.
A day in the life
· Develop models for complex business problems.
· Design and implement state-of-the-art algorithms.
· Evaluate the quality of your models and algorithms via simulations.
· Assist business stakeholders with analysis of business decisions related to your work.
· Meet with internal stakeholders to define model requirements and to share project progress and results.
· Meet with team members to discuss modeling and solution approaches.
· Attend team meetings, paper reviews, and seminars to stay up-to-date on science projects within the team, at Amazon, and in the broader scientific community.
About the team
The Middle Mile Planning Research & Optimization Science (mmPROS) organization works on research, models, algorithms, and prototypes to drive innovation within the transportation space. This position is part of the surface research team that focuses on transportation via truck, rail, or ship. Our team consists of 10+ scientists that work collaboratively on a diverse set of projects, typically in groups of 1-3 scientists. As a scientist on this team, you'll collaborate with cross-functional stakeholders and other scientists to drive innovation.
· Experience building machine learning models or developing algorithms for business application.
· 1+ years of experience programming in Java, C++, Python or related language
· PhD (OR Master's Degree plus 10+ years of industry or academic research experience) in Engineering, Technology, Computer Science, Machine Learning, Robotics, Operations Research, Statistics, Mathematics or a related quantitative field
· Detailed knowledge of applied optimization methods.
· Demonstrable work experience on optimization models that have been deployed into production systems (strategic, planning, and/or operational).
· Excellent communication skills with both technical and non-technical audiences.
· Strong problem-solving ability and the ability to work in ambiguous and constantly evolving environment.
· Strong personal interest in developing, researching, and creating new technologies with high customer impact.
· Ability to work independently and as part of a diverse team.
· Experience with mathematical programming frameworks such as Xpress, Gurobi, or Cplex.
· Experience in the design and analysis of algorithms.
· Experience with writing database queries in query languages such as SQL.
· Experience with machine learning methods such as neural networks, support vector machines, decision tree models, or clustering methods.
· Experience in discrete event or other simulation frameworks.
· Experience in network topology design.
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.