Key Responsibilities

• Design and implement end-to-end machine learning (ML) pipelines using services such as Amazon SageMaker, AWS Glue, AWS Lambda, and Amazon S3
• Perform data collection, cleaning, and feature engineering to prepare datasets for modeling
• Develop predictive models and statistical analyses using Python, R, or similar tools
• Deploy, monitor, and optimize ML models in production environments using AWS ML Ops best practices
• Collaborate with data engineers to design ETL pipelines and ensure data availability and reliability
• Utilize AWS analytics services (Athena, Redshift, QuickSight, EMR) for advanced reporting and visualization
• Work with stakeholders to translate business objectives into data science solutions and actionable insights
• Apply AI/ML algorithms for use cases such as forecasting, anomaly detection, NLP, computer vision, and recommendation systems
• Maintain compliance with security and governance standards for data management on AWS

Requirements

• 10+ years of experience in Data Science or related field
• Hands-on experience with Amazon SageMaker, AWS Glue, AWS Lambda, and Amazon S3
• Proficiency in Python, R, or similar data science tools
• Experience with AWS ML Ops best practices for deploying and monitoring models in production
• Familiarity with AWS analytics services: Athena, Redshift, QuickSight, EMR
• Strong knowledge of ETL pipeline design and data engineering collaboration
• Experience applying AI/ML algorithms: forecasting, anomaly detection, NLP, computer vision, recommendation systems
• Understanding of data governance and security compliance standards on AWS
• Work authorization: H4EAD or US Citizen

Skills Required