Description

Design and maintain end-to-end machine learning pipelines on AWS to deliver predictive models and actionable insights.

This role is hybrid.

Responsibilities

• Design and implement ML pipelines using 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 or R.
• Deploy, monitor, and optimize ML models in production using AWS MLOps best practices.
• Collaborate with data engineers to design ETL pipelines and ensure data reliability.

Required Skills

• 5+ years of experience in data science or machine learning engineering.
• Proficiency in Python and R for statistical analysis and modeling.
• Hands-on experience with Amazon SageMaker, AWS Glue, AWS Lambda, and Amazon S3.
• Experience with AWS analytics services including Redshift, Athena, QuickSight, and EMR.
• Strong understanding of ML algorithms for forecasting, anomaly detection, NLP, computer vision, or recommendation systems.
• Ability to translate business objectives into technical data science solutions.
• Bachelor’s degree in a quantitative field or equivalent experience.

Preferred Skills

• Experience implementing NLP or computer vision solutions.
• Familiarity with security and governance standards for data management on AWS.

Key Skills

Education

Any Gradute