Design, develop, and validate machine learning models for prediction, classification, segmentation, and optimization use cases.
Perform feature engineering, model selection, hyperparameter tuning, and performance evaluation.
Apply statistical and machine learning techniques to extract insights from structured and semi-structured data.
Ensure models are interpretable, reproducible, and aligned with business objectives.
Build and maintain scalable ML pipelines for training, testing, deployment, and monitoring of models.
Deploy models into production environments using batch and real-time inference patterns.
Implement model versioning, monitoring, drift detection, and retraining strategies.
Partner with the Senior Data Engineer to leverage and enhance data pipelines for ML readiness.
Collaborate with the Senior Data Architect to align model development with enterprise data architecture and governance standards.

Bachelor’s degree in Computer Science, Data Science, Machine Learning, or a related field preferred; or 6+ years of relevant experience in lieu of a degree.
Strong proficiency in Python and common machine learning libraries (e.g., scikit-learn, TensorFlow, PyTorch).
Experience developing and deploying machine learning models in production environments.
Ability to mentor more junior engineers collaboratively.
Strong proficiency with data platforms such as Snowflake and experience working with large-scale datasets.
Experience with AWS and/or Azure services supporting ML workloads (e.g., SageMaker, Azure ML, or equivalent).
Understanding of MLOps practices, including model deployment, monitoring, and lifecycle management.
Strong SQL skills and experience working with data pipelines and ETL/ELT processes.
Experience working in regulated environments, preferably healthcare, with knowledge of HIPAA and data privacy.
Ability to communicate complex technical concepts clearly to both technical and non-technical stakeholders.

Senior Machine Learning Engineer

Key skills