Amazon Applied Scientist AWS Infrastructure Services Science 

United States, Washington, Seattle 

875390118

AWS Infrastructure Services Science (AISS) researches and builds machine learning models that influence the power utilization at our data centers to ensure the health of our thermal and electrical infrastructure at high infrastructure utilization.As an Applied Scientist, you will work on our Science team and partner closely with other scientists, data engineers and Software teams to accurately model and optimize our power infrastructure. Outputs from your models will directly influence our data center topology and will drive exceptional cost savings. You will be responsible for researching and deploying machine learning models that optimize our power and thermal infrastructure, working across AWS to solve data mapping and quality issues and contribute to our Science team vision.You are skeptical. When someone gives you a data source, you pepper them with questions about sampling biases, accuracy, and coverage. When you’re told a model can make assumptions, you proactively try to break those assumptions.You have excellent business and communication skills to be able to work with product owners to understand key business questions and earn the trust of senior leaders. You will need to learn Data Center architecture and components of electrical engineering to build your models.You are comfortable juggling competing priorities and handling ambiguity. You thrive in an agile and fast-paced environment on highly visible projects and initiatives. The tradeoffs of cost savings and customer availability are constantly up for debate among senior leadership - you will help drive this conversation.Key job responsibilities
- Leverage deep expertise in one or more scientific disciplines to invent solutions to ambiguous ads measurement problems
- Disambiguate problems to propose clear evaluation frameworks and success criteria
- Work autonomously and write high quality technical documents
- Implement a significant portion of critical-path code, and partner with engineers to directly carry solutions into production
- Communicate clearly to scientific audiences

- Master's degree
- Experience programming in Java, C++, Python or related language
- Experience with SQL and an RDBMS (e.g., Oracle) or Data Warehouse
- Experience building machine learning models or developing algorithms for business application
- Experience implementing algorithms using toolkits and self-developed code
- Publication record in top ML conferences/journals such as NeurIPS, ICML, ICLR, etc.

- Experience researching about machine learning, deep learning, NLP, computer vision, data science
- PhD in computer science, mathematics, statistics, machine learning or equivalent quantitative field