Operations Technology IT
We develop and deliver technology that supports Amazon Operations at worldwide scope and scale that is unparalleled. We are forming a new team focused on providing advanced image solutions that leverage both automated and semi-automated Computer Vision (CV) and Machine Learning (ML) processing techniques to provide enhanced safety, productivity, and quality controls throughout Amazon’s global operation sites. We will be automating image capture and analysis, large data set handling, using convolutional neural networks (CNNs) for classification and segmentation of images, spatial recognition of objects/distance/velocity relationships, real time and near-term operational feedback loops and large-scale process automation. See Amazon’s recently open sourced Distance Assistant as a good example of our work.
What You’ll Do
As a highly-skilled Data Scientist II, you’ll bring your passion for quantitative data science discipline and intellectual energy to the team to help unlock valuable information found in image data and challenge the status quo and raise the bar on every service and feature we build. You’ll apply statistics, time series analysis, stochastic modeling and machine learning to solve for critical associate wellbeing assurance, process anomaly detection and defect avoidance use cases across Amazon’s worldwide network of operational facilities, utilizing streaming and static data captured through computer vision devices linked around the globe. You’ll collaborate with cross-functional team members from multiple disciplines, including software development engineers, machine learning engineers, system development engineers, business intelligence engineers, applied scientists and operations specialists to create new data-driven solutions to challenging and meaningful problems at enormous scale.
- Degree in a quantitative field (Computer Science, Mathematics, Machine Learning, AI, Statistics, etc.) plus professional experience in a data science role: MS + 2 years of experience, or BS + 4 years or experience
- Experience with data scripting languages (e.g SQL, Python, R etc.) or statistical/mathematical software (e.g. R, SAS, or Matlab, etc.)
- Ability to distill informal customer requirements into problem definitions, dealing with ambiguity and large volumes of unstructured data
- Good communication skills with both technical and business people
- Experience designing/implementing machine learning algorithms tailored to particular business needs and tested on large datasets
- Able to work independently, actively collaborate with team members and mentor others less familiar with data science principles
- High sense of ownership, self-motivation, and desire to delight customers; obsessed with quality and customer experience
- MS or PhD degree in relevant quantitative field
- Competent with data visualization software such as Tableau or Amazon Quicksight
- Have applied data science to derive meaningful and actionable information from large, unstructured data sets, such as images or video files, to digitally ingest, evaluate, classify, annotate and tag content with contextual metadata
- Experience delivering significant architecture and design aspects of new and current systems that went into production (architecture, design patterns, reliability, scalability, fault tolerance, security by design, etc.)
- Experience with primary AWS services for data (i.e. S3, RDS, DynamoDB, ElastiCache, Redshift, etc.), machine learning (SageMaker, Rekognition, etc.) and analytics (Athena, Kineses, QuickSite, etc.) and AWS Data Specialty certification a plus
- Experience with modeling sequential data, statistical forecasting, and time series models
- Familiarity with optimization models such as Linear Programming and Integer Programming
- Strong personal interest in learning, researching, and creating new technologies with high customer impact
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.