Job ID: 2992785 | Amazon Data Services, Inc.
AWS Infrastructure Services owns the design, planning, delivery, and operation of all AWS global infrastructure. In other words, we’re the people who keep the cloud running. We support all AWS data centers and all of the servers, storage, networking, power, and cooling equipment that ensure our customers have continual access to the innovation they rely on. We work on the most challenging problems, with thousands of variables impacting the supply chain — and we’re looking for talented people who want to help.
You’ll join a diverse team of software, hardware, and network engineers, supply chain specialists, security experts, operations managers, and other vital roles. You’ll collaborate with people across AWS to help us deliver the highest standards for safety and security while providing seemingly infinite capacity at the lowest possible cost for our customers. And you’ll experience an inclusive culture that welcomes bold ideas and empowers you to own them to completion. Are you an experienced in sustainability science professional who is passionate about making a big impact? Are you interested in a high impact role at the world’s preeminent cloud computing company? The AWS Sustainability Science and Innovation team is hiring a Principal Sustainability Scientist. AWS Sustainability Science and Innovation (AWS-SSI) develops world-class methodologies and models that enable data-driven decision making to enhance the near and long-term sustainability performance of AWS infrastructure and services. We use these models to develop, derisk, and accelerate the adoption of innovative sustainable technologies by leveraging strategic external partnerships, leading applied research, and building partnerships across AWS teams. By continually pushing the boundaries of sustainability science, we will strive to be a catalyst for a sustainable transformation across our customers, our partners, and the world. This role will own developing, derisking, and accelerating the adoption of innovative sustainable technologies by leveraging strategic external partnerships, leading applied research, and building partnerships across AWS teams. The ideal candidate will have industry experience in evaluating new technologies through technoeconomic assessment (TEA), Life Cycle Assessment (LCA), and have a detailed knowledge of manufacturing, design, development, and/or sourcing of data center equipment and hardware. The candidate should be comfortable working with imperfect data, identifying sources of uncertainty, and finding public data to fill the gaps where needed. The successful candidate must have strong analytical skills and the ability to apply systems thinking to complex, fast moving problems. The successful candidate will act as a subject matter expert, including designing research, gathering data, interpreting results, and developing metrics and reports. The candidate will coordinate with cross-functional project teams in gathering and analyzing large amounts of data and presenting results to senior leadership. Key job responsibilities- Advanced degree (PhD) in engineering discipline, and/or sustainability focused field.
- 10+ years of work experience and subject matter expertise in evaluating sustainable technologies through techno-economic assessment (TEA), Life Cycle Assessment (LCA), and other modeling techniques.
- Demonstrated knowledge of accelerating technology development from early stage to late stage
- 5+ years applying sustainability assessment methods to identify and execute on sustainability improvements;
- 5+ years applying TEA and LCA techniques to data center equipment and hardware and/or other products with electrical and mechanical subsystems;
- Experience with LCA modeling and databases (ecoinvent, etc.);
- Experience with prototyping parameterized models using scripting languages and AWS services;
- Demonstrated knowledge of sustainable design and manufacturing practices spanning supply chains across industries;
- Demonstrated knowledge of data structures and experience in working with large and messy datasets.
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