About the job
Amazon is hiring a Science Annotation Ops Analyst for its Worldwide Returns, ReCommerce & Sustainability team in Hyderabad. The role combines data engineering, AWS services, dashboard development and workflow automation to support machine learning annotation operations.
The position involves building data pipelines that process annotation metrics, creating production dashboards, developing SageMaker Ground Truth interfaces and automating recurring operational tasks. You will work with Program Managers, Science Specialists and other teams across the data lifecycle.
What You’ll Do
- Build and maintain data pipelines that extract, validate and consolidate annotation metrics from AWS and SageMaker.
- Develop production dashboards on EC2 using frameworks such as Plotly Dash or Streamlit.
- Set up secure cross-account AWS integrations using IAM, STS, Lambda and API Gateway.
- Create custom SageMaker Ground Truth labeling templates using HTML and JavaScript.
- Automate recurring processes such as monthly data consolidation, historical backfills and scheduled jobs.
- Maintain data quality, auditability, alerting, backups and deployment workflows.
- Use Python, pandas, boto3 and SQL to process and validate data.
- Use Git and Linux command-line tools for version control and deployment activities.
What You Need
- A Bachelor’s degree in computer science, machine learning, engineering or a related field obtained within the last 12 months, or relevant experience with data scripting languages or statistical and mathematical software.
- Experience with AWS services including EC2, Lambda, S3, DynamoDB and SQS.
- Experience building web-based dashboards using common frameworks.
Preferred Qualifications
- Experience with SageMaker Ground Truth.
- Familiarity with machine learning or annotation operations.
- Experience standardizing metrics and processes across teams.
Before You Apply
- Review Python, SQL, pandas and boto3, as these are used in the role’s data-processing work.
- Revise AWS fundamentals, particularly EC2, Lambda, S3, DynamoDB and SQS.
- Be prepared to discuss a dashboard or web-based data application you have built.
- Review HTML and JavaScript if you have experience creating web interfaces or annotation tools.
- If you are applying based on the recent-graduate qualification, make sure your degree completion date falls within the last 12 months.
Skills
Python, SQL, AWS, EC2, Lambda, S3, DynamoDB, SQS, SageMaker, JavaScript, HTML, Data Pipelines, Data Engineering, Data Dashboards, Git, Linux
Job Details
Skills
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JobsInHyd does not guarantee hiring outcomes.About Amazon
Amazon is a global technology company with businesses spanning e-commerce, cloud computing, digital services and other technology products. This position is part of Amazon’s Worldwide Returns, ReCommerce & Sustainability organization.
Amazon is hiring a Science Annotation Ops Analyst for its Worldwide Returns, ReCommerce & Sustainability team in Hyderabad. The role combines data engineering, AWS services, dashboard development and workflow automation to support machine learning annotation operations.
The position involves building data pipelines that process annotation metrics, creating production dashboards, developing SageMaker Ground Truth interfaces and automating recurring operational tasks. You will work with Program Managers, Science Specialists and other teams across the data lifecycle.
What You’ll Do
- Build and maintain data pipelines that extract, validate and consolidate annotation metrics from AWS and SageMaker.
- Develop production dashboards on EC2 using frameworks such as Plotly Dash or Streamlit.
- Set up secure cross-account AWS integrations using IAM, STS, Lambda and API Gateway.
- Create custom SageMaker Ground Truth labeling templates using HTML and JavaScript.
- Automate recurring processes such as monthly data consolidation, historical backfills and scheduled jobs.
- Maintain data quality, auditability, alerting, backups and deployment workflows.
- Use Python, pandas, boto3 and SQL to process and validate data.
- Use Git and Linux command-line tools for version control and deployment activities.
What You Need
- A Bachelor’s degree in computer science, machine learning, engineering or a related field obtained within the last 12 months, or relevant experience with data scripting languages or statistical and mathematical software.
- Experience with AWS services including EC2, Lambda, S3, DynamoDB and SQS.
- Experience building web-based dashboards using common frameworks.
Preferred Qualifications
- Experience with SageMaker Ground Truth.
- Familiarity with machine learning or annotation operations.
- Experience standardizing metrics and processes across teams.
Before You Apply
- Review Python, SQL, pandas and boto3, as these are used in the role’s data-processing work.
- Revise AWS fundamentals, particularly EC2, Lambda, S3, DynamoDB and SQS.
- Be prepared to discuss a dashboard or web-based data application you have built.
- Review HTML and JavaScript if you have experience creating web interfaces or annotation tools.
- If you are applying based on the recent-graduate qualification, make sure your degree completion date falls within the last 12 months.
Skills
Python, SQL, AWS, EC2, Lambda, S3, DynamoDB, SQS, SageMaker, JavaScript, HTML, Data Pipelines, Data Engineering, Data Dashboards, Git, Linux