Data Scientist

2 months agoSeattle, WA


Would you like to help us build the next-generation cloud services that will power the largest managed infrastructure in the world? We're hiring a Data Scientist for AWS Compute Optimizer team within Amazon AWS EC2. Our service uses large amounts of data combined with machine learning (ML) and push the boundaries of scale, availability, and performance, while maintaining the highest standards for security and operational excellence.

AWS Compute Optimizer helps you choose optimal configurations for three types of AWS resources: Amazon EC2 instances, Amazon EBS volumes, and AWS Lambda functions, based on your utilization data. By applying the knowledge drawn from Amazon's own experience running diverse workloads in the cloud, Compute Optimizer identifies workload patterns and recommends optimal AWS resources. Compute Optimizer analyzes the configuration and resource utilization of your workload to identify dozens of defining characteristics, for example, if a workload is CPU-intensive, if it exhibits a daily pattern, or if a workload accesses local storage frequently. The service processes these characteristics and identifies the hardware resource required by the workload. Compute Optimizer infers how the workload would have performed on various hardware platforms (e.g. Amazon EC2 instances types) or using different configurations (e.g. Amazon EBS volume IOPS settings, and AWS Lambda function memory sizes) to offer recommendations.

You will take on challenges in providing recommendations based on billions of metric records available for various customers and their use cases. You will be empowered to think big, invent on behalf of our customers, make judgment calls and find elegant solutions to hard problems.

Position Responsibilities:
• Build statistical and ML recommendation models
• Drive collaborative research and creative problem solving across science and engineering team
• Analyze complex datasets to drive insight
• Propose and validate hypothesis to deliver and direct our product road map
• Work with engineers to deliver low latency model predictions to production
• Constructively critique peer research and mentor junior scientists and engineers

Read more about AWS Compute Optimizer

About Us
Inclusive Team Culture
Here at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon's culture of inclusion is reinforced within our 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.
Work/Life Balance
Our team puts a high value on work-life balance. It isn't about how many hours you spend at home or at work; it's about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.
This position involves on-call responsibilities, typically for one week every two months. We don't like getting paged in the middle of the night or on the weekend, so we work to ensure that our systems are fault tolerant. When we do get paged, we work together to resolve the root cause so that we don't get paged for the same issue twice.
Mentorship & Career Growth
Our team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we're building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future.


• Master's degree in Mathematics, Statistics or in any another highly quantitative field
• 2+ years of research experience working on applied machine learning projects
• 2+ years of experience in using Python, R, or similar languages
• Deep understanding of regression modeling, forecasting techniques, time series analysis, machine-learning concepts such as supervised and unsupervised learning


• 4+ years of experience as Machine Learning Scientists, Data Scientists and/or Business Intelligence Engineers
• Experience collaborating with software engineering teams
• Track record of implementing and deploying large scale machine learning applications and tools
• Proficiency in model development, model validation and model implementation for large-scale applications
• Ability to communicate technical concepts and solutions at a level appropriate for technical and non-technical audiences
• Experience with extraction, processing, filtering, and presenting large data quantities (1M+ rows) via AWS technologies, SQL, and data pipelines
• Ability to translate business requirements into science problems. Independently define problem formulation and create scalable solutions

Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit