Role: AWS Data Scientist
Location: Charlotte, NC (Hybrid – 3 days/week from office)
Duration: 12 months
Key Responsibilities
Design, develop, and implement end-to-end Machine Learning (ML) pipelines using AWS services such as Amazon SageMaker, AWS Glue, AWS Lambda, and Amazon S3.
Collect, clean, transform, and prepare large datasets for modeling, including data preprocessing and feature engineering.
Develop and implement predictive models, statistical analyses, and machine learning solutions using Python, R, or similar programming languages.
Deploy, monitor, maintain, and optimize ML models in production environments following AWS and MLOps best practices.
Collaborate with Data Engineers to design, develop, and optimize ETL/ELT pipelines, ensuring data availability, quality, and reliability.
Leverage AWS analytics and data services such as Amazon Athena, Amazon Redshift, Amazon QuickSight, and Amazon EMR for advanced analytics, reporting, and data visualization.
Partner with business and technical stakeholders to understand business objectives and translate them into data science solutions, predictive insights, and actionable recommendations.
Apply AI/ML algorithms and techniques to solve business problems across areas such as forecasting, anomaly detection, Natural Language Processing (NLP), computer vision, and recommendation systems.
Establish and follow data security, privacy, governance, and compliance standards across AWS-based data and ML environments.
Monitor model and pipeline performance, identify issues, and implement improvements to ensure scalability, reliability, accuracy, and cost efficiency.
Document ML workflows, data pipelines, model implementations, and operational processes to support maintainability and knowledge sharing.
Thanks
Sushil Kumar