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Data Scientist, AWS Infrastructure Services Science
AMAZON-HQ2 • Herndon, Virginia, United States | Seattle, Washington, United States • onsite
FULL_TIME
Active
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Job Details
Company
amazon hq2
Location
Herndon, Virginia, United States | Seattle, Washington, United States
Work Type
onsite
Job Type
FULL_TIME
Posted
January 1, 1970
Description
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.
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 a Data Scientist, you will work on our Science team and partner closely with
other scientists and data engineers as well as Business Intelligence, Technical
Program Management, 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 building data science prototypes that optimize our power and thermal
infrastructure, working across AWS to solve data mapping and quality issues
(e.g. predicting when we might have bad sensor readings), 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 actively 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 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.
Key job responsibilities
- Proactively seek to identify opportunities and insights through analysis and
provide solutions to automate and optimize power utilization based on a broad
and deep knowledge of AWS data center systems and infrastructure.
- Apply a range of data science techniques and tools combined with subject
matter expertise to solve difficult customer or business problems and cases in
which the solution approach is unclear.
- Collaborate with Engineering teams to obtain useful data by accessing data
sources and building the ne
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