Have you ever ordered a product on Amazon and when that box with the smile arrived you wondered how it got to you so fast? Have you wondered where it came from and how much it cost Amazon to deliver it to you? If so, Amazon Logistics (AMZL), Last Mile team is for you. We manage the delivery of tens of millions of products every week to Amazon's customers, achieving on-time delivery in a cost-effective manner to deliver a smile for our customers.
As part of Amazon Last Mile Execution Analytics organization, the Data Scientist will work closely with other research scientists, machine learning experts, and economists to design and run experiments, create analyses, carry over solutions from other regions, and find new ways to improve last mile analytics to optimize the Customer experience. The Scientist will partner with technology and product leaders to solve business and technology problems using scientific approaches to build new services that surprise and delight our customers. They also work on cross-disciplinary, cross-regions efforts with other scientists within Amazon.
We are seeking candidates with robust skills in Optimization modeling (Mixed Integer Programming, Dynamic Programming, Decomposition Methods), Python coding, data collection, and analysis. A background in Machine Learning or Economics would be advantageous. The ideal candidate should possess the ability to interpret regression model coefficients in non-mathematical terms, familiarity with various probability distributions, and the capability to elucidate how actions taken by business partners influence their desired outcomes. Strong communication skills to articulate complex analytical concepts to non-technical stakeholders are essential.
Key Job ResponsibilitiesDesign 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 routing 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 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.
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