AWS SageMaker Data Scientist

Job Title: AWS SageMaker Data Scientist Location: Remote/ Raleigh NC/ Charlotte, NC/ Atlanta, GA (Hybrid) Employment type: W2 Key Responsibilities Design, build, and deploy machine learning models using AWS SageMaker (Training, Tuning, Inference, Pipelines). Develop end-to-end ML workflows including data ingestion, feature engineering, model development, and deployment. Configure and manage SageMaker Studio, SageMaker Pipelines, SageMaker Feature Store, Model Registry. Collaborate with data engineering teams to integrate ML solutions with AWS services such as S3, Lambda, Glue, Redshift, EMR, etc. Implement MLOps best practices including CI/CD for ML models, automated retraining, and monitoring using CloudWatch and SageMaker Model Monitor. Perform exploratory data analysis, feature extraction, and statistical modeling. Optimize models for accuracy, performance, and cost efficiency in AWS. Troubleshoot model performance issues and support productionized ML systems. Work closely with business stakeholders to define problem statements and translate them into actionable ML solutions. Required Skills & Experience Data Scientist / ML Engineer with strong AWS SageMaker expertise. Hands-on experience with: SageMaker Training Jobs, Inference Endpoints, Batch Transform SageMaker Pipelines for workflow automation Model Registry / Deployment / Monitoring Strong programming experience in Python, including ML libraries such as Pandas, NumPy, Scikit-learn, TensorFlow or PyTorch. Experience with AWS cloud ecosystem (S3, Lambda, IAM, Glue, ECR, ECS, CloudWatch, etc.). Knowledge of data pipelines, ETL processes, and feature engineering. Understanding of ML algorithms (supervised, unsupervised, time-series, NLP is a plus). Experience deploying ML models into production environments. Familiarity with Git, CI/CD (CodePipeline, CodeBuild) for ML workflows. Preferred Skills Experience with MLOps practices and automation. Experience with big-data tools: PySpark, EMR, Redshift. Experience working in the Banking/Financial domain (nice to have). Strong problem-solving skills and ability to work in cross-functional teams. Bachelor’s or master’s degree in computer science, Data Science, Machine Learning, Statistics, or related field.