The Network Engineering Scheduling and Technology (NEST) Science team is looking for a seasoned Sr. Product Manager, Technical (PMT) to launch and scale data science products serving network planning and scheduling needs across Amazon's middle mile transportation space.
We are a product driven science team that own a suite of predictive, optimization and simulation models that aim to shape customer package flow through our network in the most efficient manner possible.
These models simulate propagating effects of network parameters on key metrics, provide what-if analysis abilities to various teams in the organization and recommend changes for optimal network flow.
We need your expertise to position these models as science products serving complex and ever-evolving business needs within the wider Consumer organization, set and champion their long term vision and strategy, and work with program, science, engineering teams through execution and delivery.
The successful candidate will excel at digesting internal customer business problems, questioning assumptions and simplifying them beyond the operational level.
As a PMT, you will work with Scientists on the team to define the Science roadmap and facilitate the feedback loop between our Science development and the business decisions we are informing.
As the customer-facing arm of the team, you will own relationships with decision makers to help improve the end-customer experience by making the decisions that impact those end-customers more data and Science-driven.
Key job responsibilities
- Define and drive product vision and strategy, and write PR / FAQs and product strategy documents.
- Identify and define business problems in the middle mile transportation network management; think big beyond simple automation of current processes but propose product solutions that redefine how we operate our network.
- Bridge between Program, Science and Engineering teams to define requirements, scope minimum lovable products (and beyond), drive adoption.
- Work across organizational boundaries to drive product vision alignment with key stakeholders and own product communications with key stakeholders and executives.
About the team
The Network Engineering & Scheduling Technology (NEST) team focuses on network wide needle moving efforts to reduce transportation cost and improve customer experience, which are achieved through better optimization of package flow across our arcs and nodes throughout Amazon's Transportation network.
Our team enables these efforts through predictive and prescriptive analytics, providing future-looking insights on the business, predicting what is likely to happen and optimizing decision making under uncertainty.
Equipped with techniques from Operations Research, Machine Learning and Simulation, some of our models are used to govern scheduling and equipment selection of hundreds of thousands of truck movements, optimize network configurations, determine the transit times between nodes, and simulate network flow under uncertainty for informed decision making.
We build models that influence current behavior and aim to productize them establishing mechanisms to track and improve their performance, as supported by key performance indicators.
Our core team consists of Applied, Data, and Research Scientists along with technical Product Managers that come from diverse backgrounds.
BASIC QUALIFICATIONS
- Experience owning / driving roadmap strategy and definition
- Experience with feature delivery and tradeoffs of a product
- Experience contributing to engineering discussions around technology decisions and strategy related to a product
- Experience managing technical products or online services
- Experience in representing and advocating for a variety of critical customers and stakeholders during executive-level prioritization and planning
- Experience in using analytical tools, such as Tableau, Qlikview, QuickSight
- MBA or Master's degree in a quantitative field with 2+ years of relevant product management experience
PREFERRED QUALIFICATIONS
- Experience working with science teams and / or on products centered around predictive / prescriptive science models
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