About the job
Amazon’s Last Mile Team is looking for a passionate individual with strong optimization and analytical skills to join its Last Mile Science team in the endeavor of designing and improving the most complex planning of delivery network in the world. Last Mile builds global solutions that enable Amazon to attract an elastic supply of drivers, companies, and assets needed to deliver Amazon's and other shippers' volumes at the lowest cost and with the best customer delivery experience. Last Mile Science team owns the core decision models in the space of jurisdiction planning, delivery channel and modes network design, capacity planning for on the road and at delivery stations, routing inputs estimation and optimization. Our research has direct impact on Amazon customer experience, driver experience, delivery model success and the sustainable growth of Amazon. Optimizing the last mile delivery requires deep understanding of transportation, supply chain management, pricing strategies and forecasting. Only through innovative and strategic thinking, we will make the right capital investments in technology, assets and infrastructures that allows for long-term success. Our team members have an opportunity to be on the forefront of supply chain thought leadership by working on some of the most difficult problems in the industry with some of the best product managers, scientists, and software engineers in the industry.
Responsibilities
Developing solutions to better manage and optimize delivery capacity in the last mile network. Identifying and analyzing opportunities to improve existing algorithms and also on optimizing the system policies across the management of external delivery service providers and internal planning strategies. Turning high-level business requirements into mathematical models, identifying the right solution approach, and contributing to the software development for production systems. Independently mining and analyzing data, and being able to use any necessary programming and statistical analysis software to do so. Thriving in fast-paced environments, which encourage collaborative and creative problem solving, being able to measure and estimate risks, constructively critique peer research, and align research focuses with the Amazon's strategic needs. Helping coach/mentor junior scientists in the team.
Qualifications
Minimum
3+ years of building machine learning models for business application experience
PhD, or Master's degree and 6+ years of applied research experience
Experience programming in Java, C++, Python or related language
Experience with neural deep learning methods and machine learning
PhD in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field, or Master's degree and 10+ years of industry or academic research experience
5+ years of building machine learning models or developing algorithms for business application experience
5+ years of programming in Java, C++, Python or related language experience
Proficiency in model development, model validation and model implementation with Big-Data.
Strong fundamentals in problem solving, algorithm design and complexity analysis.
Proven track in leading, mentoring, and growing teams of scientists.
Excellent written and verbal communication skills with technical and business teams; ability to speak at a level appropriate for the audience. The ideal candidate can present business cases and document the models and analysis and present the results in order to influence important decisions.
Preferred
Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
Experience with large scale distributed systems such as Hadoop, Spark etc.
Have experience of building mathematical models to represent a wide range of supply chain, transportation or logistics systems.
Extensive knowledge and practical experience in several of the following areas: GenAI development, computer vision, geospatial optimization.
Work well in a fast-moving team environment and effectively deliver technical implementations having complex dependencies and requirements