Data Scientist, WWRO Data Products

Key Skills

Java Programming, Leadership, Python Programming, SQL, AWS, Machine learning techniques, Data science techniques, Natural Language Processing (NLP), Statistics, Amazon RedShift, Amazon EMR, PySpark, Amazon Elastic Compute Cloud-EC2, SPARK Programming, Lambda, AWS Kinesis, Amazon Simple Storage Service (S3), Mathematics, Terabyte scale datasets, AWS Glue

Job Description

Amazon Web Services (AWS) provides companies of all sizes with an infrastructure web services platform in the cloud (“Cloud Computing”). With AWS you can requisition compute power, storage, and many other services – gaining access to a suite of elastic IT infrastructure services as your business demands them. AWS is the leading platform for designing and developing applications for the cloud and is growing rapidly with hundreds of thousands of companies in over 190 countries on the platform.

Developers all over the world rely on our storage, compute, and virtualized services. Our success depends on our world-class selling and field teams, and these teams rely on the Worldwide Revenue Operations (WWRO) team to power their activities. We’re handling massive scale, providing data that drives the AWS business internally, and delivering products and services to help our Amazon Web Service selling teams, marketing groups, and customers.

We’re looking for a Data Scientist to design and deliver solutions that combine machine learning, human-in-the-loop input, and distributed big data technologies. We're building a cutting-edge data platform to enable us to arm our field teams with the actionable intelligence needed to engage and serve every possible AWS customer in the world, to the fullest.

This position may be based in Seattle, WA; Jersey City, NJ; Boston, MA; Arlington, VA.

Key job responsibilities

• Design solutions to complex and ambiguous data challenges, starting from first principles
• Apply Machine Learning to solve data problems, such as record matching, at scale
• Leverage company data from third-party sources in combination with internal AWS data to develop quantitative models answering critical business questions
• Build human-in-the-loop workflows, to complement and augment ML solutions
• Work with AWS machine learning and big data technologies such as Amazon Sagemaker, EMR, S3, DynamoDB, Lambda, and more
• Experiment and explore new technologies to create innovative solutions
• Use Natural Language Processing to derive insights from unstructured sources like public company regulatory filings and annual reports

About the team

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 we 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 also puts a high value on work-life balance. Striking a healthy balance between your personal and professional life is crucial to your happiness and success here, which is why we aren’t focused on how many hours you spend at work or online. Instead, we’re happy to offer a flexible schedule so you can have a more productive and well-balanced life—both in and outside of work.

BASIC QUALIFICATIONS

• Masters's degree in a quantitative field (Data Science, Statistics, Mathematics, Computer Science, Engineering)
• 3+ years of industry experience in data science and machine learning
• Proficiency in Python or modern scripting languages, with the ability to write clear, concise, explainable, testable, production-ready code
• Strong written and verbal communication skills, with experience engaging and influencing senior leaders

PREFERRED QUALIFICATIONS

• PhD in a quantitative field
• Demonstrated track-record using the scientific method to solve real-world problems by forming hypotheses and testing them through well-documented and reproducible experiments
• Experience applying statistical and machine learning models to large datasets
• Experience with AWS technologies like Glue, Redshift, EMR, S3, EC2, Kinesis, Lambda, and IAM roles and permissions
• Familiarity with Apache Spark development, either PySpark or Scala
• Experience applying Natural Language Processing (NLP) algorithms and approaches to complex, unstructured textual data sources to derive insights
• Ability to effectively articulate technical challenges and solutions
• Ability to work with ambiguous/undefined problems; ability to think abstractly
• Maturity, judgment, negotiation/influence skills, analytical skills, and leadership skills
• Meets/exceeds Amazon’s leadership principles requirements for this role
• Meets/exceeds Amazon’s functional/technical depth and complexity for this role