We develop scalable and robust state-of-the-art optimization models and algorithms, and what-if scenario planning simulation tools to make long term transportation capacity and volume planning decisions that considers varying demand patterns, costs, and operational constraints.
We are looking for an Applied Scientist with background in Operations Research, Optimization, Supply Chain, Industrial & Systems Engineering and/or Simulation.
In this role, you will have an opportunity to both develop advanced scientific solutions, and drive critical customer and business impacts. You will play a key role to drive end-to-end solutions from understanding our business requirements, exploring a large amount of historical data, building, managing and improving optimization, simulation and decision models, building prototypes and products, and exploring conceptually new solutions, to working with partner teams for prod deployment that will be used primarily for Capacity Optimization and Scenario Planning Capabilities for mid to long-term transportation volume planning.
You will collaborate closely with scientists, product management peers as well as business stakeholders.
You are an individual with outstanding analytical abilities, excellent communication skills, and are comfortable working with cross-functional teams and systems. You will be responsible for researching, prototyping, experimenting, analyzing prescriptive optimization models and developing smart automation solutions.
Key job responsibilities
- Research, design, prototype, experiment and implement advanced optimization algorithms and models to address challenging business problems by using modeling languages such as Python; participate in the production level deployment
- Collaborate with cross-functional teams including Product management, Business Intelligence Engineering, and Supply Chain/Transportation Planning to translate model insights into actionable business strategies
- Develop and maintain optimization frameworks, libraries, and tools to enable scalable and efficient optimization and automation capabilities
- Analyze optimization results, identify bottlenecks, and continuously improve the performance and accuracy of optimization models
- Stay up-to-date on the latest academic research and industry best practices in optimization techniques and tools, and identify opportunities to apply new approaches
- Create and track accuracy and performance metrics (both technical and business metrics)
- Create, enhance, and maintain technical documentation, and present to other scientists, engineers and business leaders
- Drive best practices on the team; mentor and guide junior members to achieve their career growth potential
BASIC QUALIFICATIONS
- 3+ years of building models for business application experience
- PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
- Experience in patents or publications at top-tier peer-reviewed conferences or journals
- Experience programming in Java, C++, Python or related language
- Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
- Experience in optimization: linear, non-linear, mixed-integer, large-scale, network, robust, stochastic and/or dynamic
PREFERRED QUALIFICATIONS
- Experience applying theoretical models in an applied environment
- Experience in state-of-the-art deep learning models architecture design and deep learning training and optimization and model pruning
- Experience in building optimization models and implementing them on OR tools (e.g. CPLEX, Gurobi)
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.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $136,000/year in our lowest geographic market up to $222,200/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site.