Data Scientist

Role Overview

Data Scientist (ML Engineering / MLOps Focus) partnering with a data-driven organization. This is an end-to-end ML role focused on building and deploying production machine learning systems—not only research.

What You’ll Do

• Design, build, and deploy machine learning models to solve business problems
• Own the full ML lifecycle: data preparation, feature engineering, model development, deployment, and monitoring
• Build and manage MLOps pipelines using AWS SageMaker
• Collaborate cross-functionally with engineering, operations, and product teams
• Continuously improve model performance via monitoring, retraining, and optimization
• Translate business needs into scalable, data-driven solutions

What You Bring

• 3+ years of experience in Data Science, Machine Learning Engineering, or similar
• Proven experience deploying models to production
• Strong end-to-end ML development background
• Python (required)
• Experience with ML frameworks: scikit-learn, TensorFlow, PyTorch, etc.
• Hands-on experience with AWS (especially SageMaker)
• Solid understanding of data pipelines, feature engineering, and model evaluation
• MLOps concepts including CI/CD, model versioning, and monitoring

Bonus Points

• Experience in fast-paced, business-driven environments
• Familiarity with experiment tracking tools such as MLflow (or SageMaker Experiments)

Remote / Logistics

• 100% Remote within the United States
• Work authorization required for the U.S. (no sponsorship)

Compensation

• $110,000 – $130,000 (annual)