Job Description
DESCRIPTION
The Amazon Last Mile Flex Delivery Planning Science team is looking for an Senior Research Scientist or Senior Applied Scientist with strong skills in Optimization/Operations Research. Flex is Amazon's gig economy platform for procuring drivers to satisfy the overflow demand from AMZL, as well as specialty deliveries like Sub-same Day Deliveries (SSD) and Amazon Grocery Logistics (AGL). Like most gig work platforms, drivers download the Flex app and click on offers of work in time-blocks, during which they are paid to execute deliveries from a particular warehouse, over a particular time window. Unlike other gig platforms, we allow drivers to schedule work up to a week in advance. Challenges involve scheduling drivers over time, in the presence of long lead-times, uncertainties in both demand and supply, while minimizing cost and the risks of late deliveries or excess drivers. We are also working on the integration of our driver scheduling systems with capacity planning, routing & assignment, dynamic pricing, smart offer targeting, and long-term value. We are looking for candidates with strong skills in Optimization modeling (Mixed Integer Programming, Dynamic Programming, Decomposition Methods), as well as solid skills in Python coding and data collection and analysis. Some background in Control Theory, Machine Learning, and Economics would be helpful too.
The successful candidate will be a self-starter, comfortable with ambiguity, with strong attention to detail, an ability to work in a fast-paced and ever-changing environment and a desire to help shape the overall business.
Key job responsibilities
* Design and develop advanced mathematical, optimization models and apply them to define strategic and tactical needs and drive the appropriate business and technical solutions in the areas of delivery planning, supply chain optimization, network optimization, economics, and control theory.
* Apply mathematical optimization and control techniques (linear, quadratic, SOCP, robust, stochastic, dynamic, mixed-integer programming, network flows, nonlinear, nonconvex programming, decomposition methods, model predictive control) and algorithms to design optimal or near optimal solution methodologies to be used by in-house decision support tools and software.
* Research, prototype, simulate, and experiment with these models by using modeling languages such as Python, MATLAB, Mosel or R; participate in the production level deployment.
* Create, enhance, and maintain technical documentation
* Present to other Scientists, Product, and Software Engineering teams, as well as Stakeholders.
* Lead project plans from a scientific perspective by managing product features, technical risks, milestones and launch plans.
* Influence organization's long-term roadmap and resourcing, onboard new technologies onto Science team's toolbox, mentor
other Scientists.
We are open to hiring candidates to work out of one of the following locations:
Bellevue, WA, USA
Per the internal transfers guidelines, please reach out to the hiring manager for an informational through the "Request Informational" button on the job page.
Key job responsibilities
* Design and develop advanced mathematical, optimization models and apply them to define strategic and tactical needs and drive the appropriate business and technical solutions in the areas of delivery planning, supply chain optimization, network optimization, economics, and control theory.
* Apply mathematical optimization and control techniques (linear, quadratic, SOCP, robust, stochastic, dynamic, mixed-integer programming, network flows, nonlinear, nonconvex programming, decomposition methods, model predictive control) and algorithms to design optimal or near optimal solution methodologies to be used by in-house decision support tools and software.
* Research, prototype, simulate, and experiment with these models by using modeling languages such as Python, MATLAB, Mosel or R; participate in the production level deployment.
* Create, enhance, and maintain technical documentation
* Present to other Scientists, Product, and Software Engineering teams, as well as Stakeholders.
* Lead project plans from a scientific perspective by managing product features, technical risks, milestones and launch plans.
* Influence organization's long-term roadmap and resourcing, onboard new technologies onto Science team's toolbox, mentor
other Scientists.
BASIC QUALIFICATIONS
- 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
- BASIC QUALIFICATIONS
- - PhD or equivalent Master's Degree, four years experience in Operations Research, Industrial Engineering, Control Engineering, Computer Science, or related field
- - Expertise in optimization: linear, non-linear, mixed-integer, large-scale, network, robust, stochastic, decomposition methods
- - Expertise in building optimization models using XPRESS, Gurobi, CPLEX.
- - Expertise in validating, simulating math optimization models
- - Proficient coding in Python or related language
- - Strong communication, documentation skills
- - Understanding of forecasting methods
Job Tags
Full time, Flexible hours,