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 passion for excellence. The wrong choice of data could cost the business dearly. You maintain rigorous standards and take ownership of the outcome of your data pipelines and code. You raise the bar on the software code standards to delivery faster and efficient solutions.

You do whatever it takes to add value. You don't care whether you're building complex ML models, writing blazing fast code, integrating multiple disparate data-sets, or creating baseline models - you care passionately about stakeholders and know that as a curator of data insight you can unlock massive cost savings and preserve customer availability.

You have a limitless curiosity. You constantly ask questions about the technologies and approaches we are taking and are constantly learning about industry best practices you can bring to our team.

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.

This position requires superior analytical thinkers, able to quickly approach large ambiguous problems and apply their technical and statistical knowledge to identify opportunities for further research. You should be able to independently mine and analyze data, and be able to use any necessary programming and statistical analysis software to do so. Successful candidates must thrive in fast-paced environments which encourage collaborative and creative problem solving, be able to measure and estimate risks, constructively critique peer research, and align research focuses with the Amazon's strategic needs.

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

- Work closely with other scientists to deliver impactful customer solutions

- Share and publish works with the broader scientific community through meetings and conferences

- Communicate clearly to scientific audiences

BASIC QUALIFICATIONS

- 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

- Have publications on top-tier conferences, such as CVPR, ICCV, ECCV or NeurIPS

PREFERRED QUALIFICATIONS

- Experience researching about machine learning, deep learning, NLP, computer vision, data science

- PhD in computer science, mathematics, statistics, machine learning or equivalent quantitative field

The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.

USA, VA, Arlington - 136,000.00 - 184,000.00 USD annually

USA, VA, Herndon - 136,000.00 - 184,000.00 USD annually

USA, WA, Seattle - 136,000.00 - 184,000.00 USD annually