Staff Machine Learning Engineer

Staff Machine Learning Engineer at Becton Dickinson will lead design, development, and deployment of ML solutions across the Connected Care portfolio, owning the full lifecycle from experimentation to production. The role combines hands-on technical leadership with mentorship, applying classical and deep learning methods to forecasting, optimization, and anomaly detection in a regulated healthcare environment.

Responsibilities

• Design, train, and validate demand forecasting, optimization, and anomaly detection models on product and operational data
• Build and maintain end-to-end training pipelines including feature engineering, dataset versioning, and experimentation with rigorous backtesting
• Deploy models as production AWS services with clean, modular, well-tested code and implement monitoring for quality, latency, and drift
• Develop explainable model outputs and user-facing interfaces so stakeholders can understand and act on recommendations
• Mentor ML engineers, review technical designs and code, and establish engineering standards and best practices across the team

Requirements

• Bachelor's in Machine Learning, Statistics, Operations Research, Applied Math, Computer Science, or related field
• 7+ years hands-on ML/AI and software engineering with production ownership of forecasting or optimization systems
• Deep expertise in classical ML, deep learning, and time-series modeling across hierarchical and multi-series settings
• Expert Python proficiency with NumPy, pandas, scikit-learn, statsmodels, PyTorch, and forecasting libraries
• 3+ years building ML services on AWS (EKS, S3, Lambda, EventBridge, Step Functions) with Docker and Kubernetes
• Strong software engineering practices: testing, CI/CD, code review, observability, and Agile/Scrum experience
• Experience documenting methodology, assumptions, and validation for regulatory and clinical review