The Amazon Air Science and Technology team is seeking an Applied Scientist to be part of a team solving complex aviation operations problems to reduce cost and improve performance. This is a blue-sky role that gives you a chance to bring optimization modeling, statistical modeling, machine learning advancements to data analytics for customer-facing solutions in complex industrial settings.
You will work closely with product, research science and technical leaders throughout Amazon Air, Amazon Delivery Technology and Supply Chain Optimization and will be responsible for influencing funding decisions in areas of investment that you identify as critical future product offerings. You will partner with software developers and data scientists to build end-to-end data pipelines and production code, and you will have exposure to senior leadership as we communicate results and provide scientific guidance to the business. You will analyze large amounts of business data, build the machine learning or optimization models that will enable us to continually delight our customers worldwide.
The ideal candidate will have extensive experience in Science work, business analytics and have the aptitude to incorporate new approaches and methodologies while dealing with ambiguities. Excellent business and communication skills are a must to develop and define key business questions and build models that answer those questions. You should have a demonstrated ability to think strategically and analytically about business, product, and technical challenges. Further, you must have the ability to build and communicate compelling value propositions, and work across the organization to achieve consensus. This role requires a strong passion for customers, a high level of comfort navigating ambiguity, and a keen sense of ownership and drive to deliver results.
· Partnership with the engineering and operations to drive modeling and design for complex business problems.
· Design and prototype decision support tools (product) to automate standardized processes and optimize trade-offs across the full decision space.
· Contribute to the mid- and long-term strategic planning studies and analysis.
· Lead complex transportation modeling analyses to aid management in making key business decisions and set new policies.
· Ph.D. in Machine Learning, Operations Research, Transportation Management, Operations Management, Industrial Engineering, Systems Engineering, Computer Science, Applied Mathematics, Statistics or a related field.
· Experience designing/implementing machine learning algorithms tailored to particular business needs and tested on large datasets.
· Detailed knowledge of optimization methods to solve linear and integer programming models.
· Experience designing simulation and optimization solution for large scale applications.
· Experience in writing scripts (Perl, Ruby) to manipulate data and developing software applications in programming languages such as C++, Java, and Python.
· The ability to implement models and tools through the use of high-level modeling languages (e.g. AMPL, Mosel, R, Matlab, Julia) is a plus.
· 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.
· Ability to work independently and as part of a diverse team.
· 5+ yrs of relevant, broad research science experience after PhD degree.
· Experience in data mining and using databases in a business environment with large-scale, complex datasets.
· Strong personal interest in learning, researching, and creating new technologies with high customer impact.
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.