Managing trillions of objects in storage, retrieving them in sub-x ms, new features that deploy to hundreds of thousands of hosts, achieving 99.999999999% durability. These are just a few of the numbers that give you a sense of the scale of the exciting problems you will find every day working in Simple Storage Service (S3). Amazon S3 powers businesses across the globe that make the lives of consumers better daily. Whether its electronic content delivered to your home, technology that betters your remote working experience, allows you to plan travel to exotic places or simply get stuff delivered to your home. As a Data Scientist in S3, you will be able to dive deep into some of the most interesting and complex problems at the largest scale.
We are seeking an innovative and technically strong data scientist with a background in performance optimization, machine learning, and statistical modeling/analysis. This role requires a team member to have strong quantitative modeling skills and the ability to apply optimization/statistical/machine learning methods to complex decision-making problems, with data coming from various data sources. The candidate should have strong communication skills, be able to work closely with stakeholders and translate data-driven findings into actionable insights. The successful candidate will be a self-starter, comfortable with ambiguity, with strong attention to detail and ability to work in a fast-paced and ever-changing environment.
This role will sit in our new headquarters in Northern Virginia, where Amazon will invest $2.5 billion dollars, occupy 4 million square feet of energy efficient office space, and create at least 25,000 new full-time jobs. Our employees and the neighboring community will also benefit from the associated investments from the Commonwealth including infrastructure updates, public transportation improvements, and new access to Reagan National Airport.
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.
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.
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.
· Bachelor’s or Master’s Degree in a quantitative field (such as Computer Science, Machine Learning, Operational Research, Statistics, Mathematics) or equivalent experience
· 8 + years of industry experience in predictive modeling, data science and analysis
· 3 + years experience in a ML or data scientist role with a track record of building ML or DL models
· 3 + years experience using Python, SQL, or R; knowledge of Spark/ML
· PhD in a quantitative field (Computer Science, Machine Learning, Operational Research, Statistics, Mathematics)
· Ability to develop experimental and analytic plans for data modeling processes, use of strong baselines, and determine cause and effect relations
· Publications or presentations at Machine Learning, Deep Learning and Data Mining journals/conferences
· Experience with AWS technologies like Redshift, S3, EC2, Data Pipeline, & EMR
· Experience using ML libraries, such as scikit-learn, caret, mlr, or mllib
Amazon is committed to a diverse and inclusive workplace.View More
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