5 Research Scientist II, Modeling and Optimization (MOP) Jobs in Bellevue, WA
Amazon is Hiring a Research Scientist II, Modeling and Optimization (MOP) Near Bellevue, WA
DESCRIPTION
Do you enjoy solving challenging problems and driving innovations in research? Are you seeking for an environment with a group of motivated and talented scientists like yourself? Do you want to create scalable optimization models and apply machine learning techniques to guide real-world decisions? Do you want to play a key role in the future of Amazon transportation and operations? North America Sort Centers (NASC) are experiencing growth and looking for a skilled, highly motivated Research Scientist in partnership with the Modeling and Optimization (MOP) team.
The Sort Center network is the critical Middle-Mile solution in the Amazon Transportation Services (ATS) group, linking Fulfillment Centers to the Last Mile. The experience of our customers is dependent on our ability to efficiently execute volume flow through the middle-mile network.
Key job responsibilities
A Research Scientist
- provides analytical decision support to Amazon planning teams via applying advanced mathematical and statistical techniques.
- collaborates effectively with Amazon internal business customers, and is their trusted partner
- is proactive and independent in discovering and resolving business pain-points within a given scope
- is able to identify a suitable level of sophistication in resolving the different business needs
- is confident in leveraging existing solutions to new problems where appropriate and is independent in designing and implementing new solutions where needed
- is aware of the limitations of their proposed solutions and is proactive in communicating them to the business, and advances the application of sciences towards Amazon business problems by bringing new methods, ideas, and practices to the team and scientific community.
A day in the life
- Your will be developing model-based optimization, simulation, and/or predictive tools to identify and evaluate opportunities to improve customer experience, network speed, cost, and efficiency of capital investment.
- You will quantify the improvements resulting from the application of these tools and you will evaluate the trade-offs between potentially competing objectives.
- You will develop good communication skills and ability to speak at a level appropriate for the audience, will collaborate effectively with fellow scientists, software development engineers, and product managers, and will deliver business value in a close partnership with many stakeholders from operations, finance, IT, and business leadership.
About the team
- At the Modeling and Optimization (MOP) team, we use mathematical optimization, algorithm design, statistics, and machine learning to improve decision-making capabilities across WW Operations from first mile to last mile.
- We focus on transportation topology, labor and resource planning for fulfillment facilities, routing science, visualization research, data science and development, and process optimization.
- We create models to simulate, optimize, and control the fulfillment network with the objective of reducing cost while improving speed and reliability.
- We support multiple business lanes, therefore maintain a comprehensive and objective view, coordinating solutions across organizational lines where possible.
We are open to hiring candidates to work out of one of the following locations:
Bellevue, WA, USA
BASIC QUALIFICATIONS
- PhD or equivalent Master's Degree plus 4 years of experience in Operations Research, Industrial Engineering, Computer Science, Chemical Engineering, Transportation Engineering, Statistics, Applied Mathematics, or related field
- Experience developing optimization models and algorithmic solutions tailored to particular business problems with state-of-the-art optimizers (e.g., CPLEX, Gurobi, XPRESS)
- Experience writing scripts to manipulate data and developing software in traditional programming languages (e.g., C/C , Java, Python, SQL).
- Excellent communication skills with both technical and non-technical audiences.
PREFERRED QUALIFICATIONS
- Relevant work experience with demonstrated impact in decision support applications (e.g., scheduling, vehicle routing, facility location, assignment problems, revenue management)
- Practical experience developing large-scale decision support tools based on optimization technology and control theory
- Familiarity with large-scale simulation modeling
- Familiarity with stochastic processes and optimization under uncertainty
- Familiarity with forecasting and time series analysis
- Familiarity with machine learning techniques and technologies
- Familiarity with data visualization (e.g. R, Tableau, GIS software)
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.
Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $124,100/year in our lowest geographic market up to $212,800/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. Applicants should apply via our internal or external career site.
Job Summary
Full Time
Transportation
$132k-165k (estimate)
02/09/2024
05/04/2024
RANDALLSTOWN, MD
25 - 50
2018
$50M - $200M
Transportation
