Lead Data Scientist
Johnson & Johnson
June 24, 2026
Remote friendly (Spring House, PA)
United States
IT
Lead Data Scientist (MedTech Supply Chain Digital)
Responsibilities:
- Lead end-to-end AI/ML and GenAI projects: problem framing, requirements, data prep, model development, solution architecture, production deployment, and post-deployment monitoring/optimization.
- Architect and deliver ML solutions for complex, cross-functional initiatives across J&J business segments.
- Design, build, and deploy agentic AI systems (multi-agent orchestration, tool-use frameworks, RAG, autonomous decision-making workflows).
- Run POCs and rapidly prototype ML/GenAI digital products, translating them into production-grade solutions.
- Partner with product, engineering, and business stakeholders to define product vision, technical roadmaps, and measurable business value.
- Provide technical leadership/mentorship; conduct code/model reviews and establish best practices.
- Author/review technical documents and contribute to knowledge sharing/thought leadership.
- Continuously evaluate emerging AI tech (foundation models, agentic frameworks, multi-modal AI) and drive adoption.
- Establish standards for model governance, responsible AI, reproducibility, and documentation.
Qualifications:
- PhD (1+ years) or MS (3+ years) in data science; hands-on building/deploying large-scale ML in cloud (Azure preferred).
- Expertise in LLMs/Generative AI/agentic AI; hands-on with LangChain/LangGraph/Semantic Kernel/AutoGen/CrewAI or similar.
- Production deployment experience (model serving, APIs, scalability, monitoring).
- Strong Python; ML libraries (PyTorch/TensorFlow/scikit-learn/Hugging Face) and data tools (Spark, SQL, Pandas).
- Experience with prompt engineering, LLM fine-tuning, RAG, embeddings, and evaluation.
Preferred:
- MLOps/DevOps, CI/CD, model versioning, MLflow/W&B, Docker/Kubernetes, infrastructure-as-code.
- Azure AI services (Azure OpenAI, Azure AI Foundry, Azure ML, Cognitive Services).
- Responsible AI (fairness, explainability, bias, governance).
- Multi-functional stakeholder partnership; Demand/Supply Planning/Supply Chain domain; streaming/event-driven AI; publications/patents.
Benefits (as stated): Medical, dental, vision, life insurance, short/long-term disability, business accident insurance, group legal; retirement and 401(k); long-term incentive; paid time off (vacation, sick time, holidays), parental leave, and other specified leaves.
Responsibilities:
- Lead end-to-end AI/ML and GenAI projects: problem framing, requirements, data prep, model development, solution architecture, production deployment, and post-deployment monitoring/optimization.
- Architect and deliver ML solutions for complex, cross-functional initiatives across J&J business segments.
- Design, build, and deploy agentic AI systems (multi-agent orchestration, tool-use frameworks, RAG, autonomous decision-making workflows).
- Run POCs and rapidly prototype ML/GenAI digital products, translating them into production-grade solutions.
- Partner with product, engineering, and business stakeholders to define product vision, technical roadmaps, and measurable business value.
- Provide technical leadership/mentorship; conduct code/model reviews and establish best practices.
- Author/review technical documents and contribute to knowledge sharing/thought leadership.
- Continuously evaluate emerging AI tech (foundation models, agentic frameworks, multi-modal AI) and drive adoption.
- Establish standards for model governance, responsible AI, reproducibility, and documentation.
Qualifications:
- PhD (1+ years) or MS (3+ years) in data science; hands-on building/deploying large-scale ML in cloud (Azure preferred).
- Expertise in LLMs/Generative AI/agentic AI; hands-on with LangChain/LangGraph/Semantic Kernel/AutoGen/CrewAI or similar.
- Production deployment experience (model serving, APIs, scalability, monitoring).
- Strong Python; ML libraries (PyTorch/TensorFlow/scikit-learn/Hugging Face) and data tools (Spark, SQL, Pandas).
- Experience with prompt engineering, LLM fine-tuning, RAG, embeddings, and evaluation.
Preferred:
- MLOps/DevOps, CI/CD, model versioning, MLflow/W&B, Docker/Kubernetes, infrastructure-as-code.
- Azure AI services (Azure OpenAI, Azure AI Foundry, Azure ML, Cognitive Services).
- Responsible AI (fairness, explainability, bias, governance).
- Multi-functional stakeholder partnership; Demand/Supply Planning/Supply Chain domain; streaming/event-driven AI; publications/patents.
Benefits (as stated): Medical, dental, vision, life insurance, short/long-term disability, business accident insurance, group legal; retirement and 401(k); long-term incentive; paid time off (vacation, sick time, holidays), parental leave, and other specified leaves.