We are the people who give possibilities purpose

BD is one of the largest global medical technology companies in the world. Advancing the world of health™ is our Purpose, and it’s no small feat. It takes the imagination and passion of all of us—from design and engineering to the manufacturing and marketing of our billions of MedTech products per year—to look at the impossible and find transformative solutions that turn dreams into possibilities.

Job Description

Summary:

As a Staff Machine Learning Engineer, you will lead the design, development, and deployment of machine learning solutions that power AI-driven decision-making across the BD Connected Care portfolio. You will build and operationalize models for forecasting, optimization, anomaly detection, and other advanced analytics use cases, transforming high-quality data into scalable, intelligent products.

This is a hands-on technical leadership role where you will own the end-to-end machine learning lifecycle, from experimentation and model development to deployment, monitoring, and continuous improvement. You will help define the architecture, engineering standards, and best practices that shape the future of BD's AI platform while collaborating across engineering, product, and clinical teams to deliver impactful solutions.

The ideal candidate combines deep machine learning expertise with strong software engineering skills, takes ownership of complex technical challenges, and is motivated by BD's mission to improve patient outcomes through innovation and AI.

Key Responsibilities:

• Modeling Ownership: Own the design, training, and validation of demand forecasting, optimization, and anomaly detection models across product, operational, and clinical-adjacent data. Build probabilistic, multi-series models that emit prediction intervals rather than point estimates, so recommendations can be set to a defined service-level target. 

• Methods Breadth:Apply classical methods (ARIMA/ETS/state-space, Bayesian, gradient boosting, hierarchical forecasting, LP/MIP/convex optimization) and deep learning approaches (transformer-based and other neural forecasting architectures, foundation-model adaptation), choosing based on the problem. 

• Training and Experimentation:Own training pipelines end to end, including feature engineering, dataset versioning, and reproducibility. Build rigorous experimentation with temporally aware splits, honest baselines against the incumbent rule-based system, ablations, calibration, and uncertainty quantification. Design back tests and shadow-mode evaluations that precede any production trust. 

• Production:Turn models into production services on AWS (EKS, S3, Lambda, EventBridge, Step Functions): inference services, batch jobs, and data integrations. Implement clean, modular, well-tested code with type hints, unit and integration tests, dependency hygiene, and reproducible builds. 

• Evaluation and Monitoring: Implement model evaluation and continuous-evaluation harnesses, instrument production inference for quality, latency, and drift, and partner with ML Platform on packaging, deployment, promotion gates, and rollback. 

• Explainability: Develop explainable outputs that surface why a recommendation was made, so users can accept, override, or investigate with confidence. 

• Mentorship and Standards:Mentor AI and ML engineers, review designs and code, and contribute to engineering standards and team-wide tooling. 

• Documentation:Document methodology, assumptions, and validation in Confluence; produce design docs, ADRs, runbooks, and model cards suitable for regulatory and clinical review. Documentation is a deliverable, not an afterthought. 

Required Qualifications

• Bachelor's in Machine Learning, Statistics, Operations Research, Applied Math, Computer Science, or related field. 

• 7+ years of hands-on ML/AI and software engineering with production ownership, including delivery of forecasting and/or optimization systems in production. 

• Deep expertise in model development and training across both classical ML and deep learning, including time-series and hierarchical or multi-series settings. 

• Excellent Python; fluent with NumPy, pandas, scikit-learn, statsmodels, PyTorch, and a forecasting stack (Prophet, GluonTS, Nixtla, or PyTorch Forecasting). 

• Track record of moving models from research code into production environments, beyond notebooks and prototypes, including 3+ years building ML services on AWS without relying on SageMaker as the primary ML platform. Comfortable with Docker, Kubernetes-based deployment, and GitHub Actions CI/CD. 

• Strong software-engineering habits: testing, packaging, CI, code review, observability.  
• Working in Agile/Scrum and documenting in Confluence. 

Preferred Qualifications:

• Master's or PhD in Machine Learning, Statistics, Operations Research, Applied Math, Computer Science, or related field.
• Experience in healthcare, pharmacy, supply chain, or other regulated domains, and in environments with formal change-control and validation requirements. 

• Background in mathematical optimization (LP/MIP/convex/stochastic) and integration with ML predictions. 

• Experience with foundation models for time-series (TimesFM, Chronos, Moirai) and structured adaptation for tabular and temporal tasks. 

• Familiarity with causal inference, counterfactual evaluation, and uplift modeling. 

• Hands-on experience building AI agents and agentic workflows using orchestration frameworks (LangChain, LangGraph, LlamaIndex, CrewAI), including tool and function calling, guardrails, and error recovery. 

• Experience with RAG architectures, vector databases (Pinecone, Weaviate, pgvector), and systematic eval tooling (LangSmith or similar). 

• Experience with model serving (vLLM, Triton, TorchServe) and inference optimization (quantization, ONNX, TensorRT). 

• Experience with experiment tracking and model registries (MLflow, Weights & Biases). 

• Professional use of AI-assisted development tools (Claude Code, Copilot, Cursor) with strong judgment around correctness, security, licensing, and review discipline. 

• Publications in top ML/AI conferences (NeurIPS, ICML, KDD, INFORMS, MSOM). 

Team Culture

We are building a high-ownership, mission-driven team, energized by the opportunity to solve hard problems that make a difference in patients' lives. 

• High ownership - You take full responsibility for outcomes. You identify problems early and drive them to resolution, taking initiative rather than waiting for direction. 

• Entrepreneurial spirit - You think and act like an owner: resourceful, creative, willing to challenge assumptions, and consistently focused on impact. You find practical paths forward through difficult problems. 

• Mission-driven work ethic - You are motivated by the work we are doing to advance the world of health and improve patient outcomes, and you bring genuine energy and commitment to that mission. You are driven by a real sense of purpose in your work. 

• Creative problem-solving - You are comfortable working through ambiguity. You dig in, experiment, iterate, and deliver, and you are energized by the challenge of figuring things out and finding the right solution. 

• AI-native mindset - You actively use the latest AI tools to accelerate your own work across coding, research, design, and documentation, and you are eager to keep advancing what is possible with AI in your craft. 

Why Join Us?

To find purpose in the possibilities, we need people who can see the bigger picture, who understand the human story that underpins everything we do. We welcome people with the imagination and drive to help us reinvent the future of healthcare. At BD, you’ll discover a culture in which you can learn, grow and thrive.

We believe that when people connect in person, we learn faster, collaborate more deeply, and build a stronger culture. Join us and enjoy a culture where face-to-face collaboration supports your learning, your progress, and your success.

To learn more about BD visit https://bd.com/careers.

Becton, Dickinson, and Company is an Equal Opportunity Employer. We evaluate applicants without regard to race, color, religion, age, sex, creed, national origin, ancestry, citizenship status, marital or domestic or civil union status, familial status, affectional or sexual orientation, gender identity or expression, genetics, disability, military eligibility or veteran status, and other legally protected characteristics.

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Our pay is based on the role and the necessary skills and education to perform it successfully. The salary or hourly rate offered is determined by the role's specific requirements, including any applicable step rate pay system at the work location. Salary or hourly pay ranges are influenced by labor laws and Collective Bargaining Agreement (CBA) requirements applicable to the work location which may also affect the workplace arrangement of the role.

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