AWS ML Engineer

Data Scientist

Data Analysis and Exploration: Analyze large, complex datasets to extract meaningful insights and identify trends. Perform exploratory data analysis (EDA) using AWS data processing tools.

Model Development: Build, train, and evaluate machine learning models using AWS services such as SageMaker, and frameworks like TensorFlow.

ETL and Data Preparation: Work with AWS Glue, Redshift, Textract and other data engineering tools to preprocess, transform, and manage data for machine learning purposes.

Machine Learning Pipeline Development: Develop end-to-end machine learning pipelines on AWS to automate and operationalize the deployment of models at scale.

Collaboration: Work closely with data engineers, business analysts, and stakeholders to understand business needs and tailor data science solutions to meet those needs.

Model Deployment and Monitoring: Deploy models to production and set up monitoring systems to track performance, accuracy, and other key metrics. Use SageMaker and Lambda for model hosting and API development.

Documentation and Reporting: Document models, processes, and findings for stakeholders, enabling clear communication of results and decision support.