As a Sr. Applied Scientist on the AMZL Energy Sciences team, you’ll apply your knowledge of electric vehicles (EVs) and couple that with a background in modeling, data science, optimization, and software development to help us develop and deploy our Last Mile electrified delivery fleet and charging infrastructure.
You’ll have the opportunity to define our end to end electrification system - from the vehicles all the way back to the chargers, ensuring our fleet is successful as we scale.
This role is inherently cross-functional and requires working closely with software teams, research teams, operations teams, and product management teams to help deploy our EVs into an electrified Last Mile network.
Key job responsibilities
- Develop a deep understanding of how EVs use energy on delivery routes, and how they perform over their life as the battery ages.
- Build system level modeling and simulation tools to develop EV and charging infrastructure requirements, optimizing for cost and reliability.
- Engage with other science teams in Last Mile, educating them on our electrification work and contributing technical knowledge to software development teams.
About the team
The AMZL Energy Sciences team was formed to guide the Global Fleet and Products organization's efforts to electrify our Last Mile delivery fleet.
We leverage modeling, simulation, and optimization to help the business make the right choices at the right time, ensuring the fleet and charging infrastructure we deploy is operationally successful and cost-optimized.
We provide our stakeholders with a deep understanding of how the network is performing as we scale, and provide data-driven solutions to problems as they arise.
BASIC QUALIFICATIONS
- PhD, or Master's degree and 6+ years of applied research experience
- Experience programming in Java, C++, Python or related language
- Understanding of energy and power concepts, and knowledge of physics related to energy consumption in transportation applications.
- Knowledge of optimization techniques (IP, MIP).
PREFERRED QUALIFICATIONS
- Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
- Direct experience in building models for electric vehicle performance.
- Experience with EV charging system design, and understand of utility electricity rate structures (time of use charges, demand charges).
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