Science Annotation Ops Analyst

Are you passionate about building scalable data pipelines and dashboards that power machine learning innovation Do you thrive at the intersection of data engineering AWS cloud architecture and automation Join the Worldwide Returns ReCommerce & Sustainability team where your technical expertise will directly support high-quality training data that fuels ML modelshelping Amazon achieve zero waste zero cost of returns and zero defects.

As a Science Annotation Operations Analyst youll own end-to-end data pipelines that extract validate and consolidate annotation metrics from AWS and SageMaker. Youll build production dashboards create custom annotation interfaces and automate critical workflowsall while collaborating with Program Managers Science Specialists and cross-functional teams. This role offers you the opportunity to leverage Python AWS services and web development skills to solve real problems that impact both customers and the planet.

Key job responsibilities
- Design and maintain scalable data pipelines that extract parse validate and consolidate annotation metrics from AWS and SageMaker ensuring data quality and auditability throughout the lifecycle
- Build and operate production dashboards on EC2 covering the full data lifecycle (ingest validate score publish) using technologies like Plotly Dash or Streamlit
- Implement secure least-privilege cross-account AWS integrations using IAM STS Lambda and API Gateway to enable seamless data ingestion from multiple source accounts
- Develop custom SageMaker Ground Truth labeling templates using HTML and JavaScript with conditional logic that transform written SOPs into validated annotation interfaces
- Automate recurring manual processes including monthly consolidation historical backfills and scheduled jobs while maintaining alerting backups and deployment workflows to improve operational efficiency

A day in the life
Youll collaborate with Program Managers Leads and Science Annotations Specialists to tackle technical challenges that span the data lifecycle. You could start with debugging a Python script that processes annotation metrics with pandas and boto3 followed by deploying a new dashboard feature to EC2. Later you could be writing SQL queries to validate data quality configuring IAM policies for secure cross-account access or building a new annotation UI template in HTML and JavaScript. Youll use Git for version control and Linux command-line tools to manage deployment workflows all while serving as the technical POC for your team.

About the team
The Worldwide Returns ReCommerce & Sustainability team is dedicated to making zero happenzero cost of returns zero waste and zero defects. Were an agile and inclusive organization that innovates to create long-term value by investing in our people and our planet. Our team spans business product operations data and software engineering disciplines working together to manage the lifecycle of returned and damaged products.

Youll partner across teams to help customers discover great deals on quality used and open box items improve the returns experience and reduce waste in reverse logistics. As part of this mission-driven team youll be a builder and an owner collaborating cross-functionally to design scalable solutions. At Amazon Earth is our customer toojoin us and help innovate for a more sustainable future.

- Bachelors degree within last 12 months in computer science machine learning engineering or related fields or experience with data scripting languages (e.g. SQL Python R or equivalent) or statistical/mathematical software (e.g. R SAS Matlab or equivalent)
- Experience with AWS Services including EC2 Lambda S3 DynamoDB SQS
- Experience building web based dashboards using common frameworks

- SageMaker Ground Truth experience
- ML / annotation-operations domain familiarity
- Experience standardizing metrics and processes across teams

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