Data Science

· Cloud-based Data Science: Design, develop, and deploy machine learning models using AWS services like Amazon SageMaker, AWS Lambda, AWS Glue, and others.

· Data Processing & Integration: Utilize AWS tools such as AWS Redshift, AWS S3, AWS EMR, and AWS Glue to manage and process large-scale datasets. Design and maintain data pipelines for efficient data collection, transformation, and storage.

· Model Development: Build, train, and fine-tune machine learning models using AWS SageMaker or custom frameworks. Implement supervised and unsupervised learning algorithms, and optimize model performance.

· Scalability & Automation: Scale machine learning workflows and automate tasks using AWS technologies to improve efficiency and reduce manual intervention.

· Cloud Security & Best Practices: Ensure models and data are securely managed in the cloud, following AWS security best practices and governance frameworks.

· Collaboration & Communication: Work closely with data engineers, DevOps, product teams, and business stakeholders to define project requirements and deliver impactful data science solutions.

· Experimentation & A/B Testing: Conduct A/B testing, model validation, and experimentation to assess model performance and derive insights.

· Continuous Learning & Innovation: Stay updated with the latest AWS technologies, machine learning algorithms, and best practices to ensure cutting-edge solutions