Lead Data Scientist
Johnson & Johnson
June 24, 2026
Remote friendly (Raritan, NJ)
United States
IT
Purpose
Lead Data Scientist for MedTech Supply Chain Digital, designing, developing, and deploying production-grade GenAI/AI solutions using Generative AI, LLMs, agentic AI systems, classical machine learning, computer vision, and end-to-end ML delivery.
You Will Be Responsible For
- Collaborating across teams to shape a cohesive digital product and technology strategy enabling product-led transformation and a scalable, reusable, modular, cost-effective digital ecosystem.
In Addition, You Will
- Lead end-to-end AI/ML and GenAI projects from problem framing and requirements through data prep, model development, architecture, production deployment, and post-deployment monitoring/optimization.
- Architect and deliver ML solutions for complex, cross-functional projects across business segments.
- Design, build, and deploy agentic AI systems (multi-agent orchestration, tool-use frameworks, RAG, autonomous decision-making workflows).
- Conduct POCs and rapidly prototype ML/GenAI digital products, translating POCs into production-grade solutions.
- Partner with product management, engineering, and business stakeholders to define product vision, develop technical roadmaps, and deliver measurable business value.
- Provide technical leadership and mentorship; conduct code/model reviews and establish best practices.
- Author/review technical proposals, design documents, and reports; contribute to internal/external AI/ML knowledge sharing.
- Continuously evaluate and adopt emerging AI technologies (foundation models, agentic frameworks, multi-modal AI).
- Establish/enforce model governance, responsible AI practices, reproducibility, and documentation.
Requirements / Qualifications
Required:
- PhD (1+ years) or MS (3+ years) hands-on data science experience deploying large-scale ML in cloud (Azure preferred).
- Hands-on expertise in LLMs/Generative AI/agentic AI with frameworks (LangChain/LangGraph/Semantic Kernel/AutoGen/CrewAI or similar).
- Production deployment experience (model serving, APIs, scalability, monitoring).
- Python; ML libraries (PyTorch/TensorFlow/scikit-learn/Hugging Face) and data tools (Spark/SQL/Pandas).
- Depth in agentic AI (multi-agent systems, tool use, planning/reasoning, memory, human-in-the-loop).
- Prompt engineering, LLM fine-tuning, RAG pipelines, embeddings, and generative AI evaluation.
- Strong communication to explain complex concepts to non-technical stakeholders.
Preferred:
- MLOps/DevOps (CI/CD, 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 detection, governance).
- Multi-functional delivery with business adoption/change management.
- Demand/Supply Planning, Manufacturing, or Supply Chain domain experience.
- Real-time/streaming AI or event-driven architectures.
- Publications/patents in AI/ML.
Benefits (if applicable)
- Medical, dental, vision, life insurance, short- and long-term disability, business accident insurance, group legal insurance; retirement plan and 401(k); long-term incentive program; time-off benefits including vacation, sick time, holidays, work/personal/family time, parental leave, and other specified leave types.
Lead Data Scientist for MedTech Supply Chain Digital, designing, developing, and deploying production-grade GenAI/AI solutions using Generative AI, LLMs, agentic AI systems, classical machine learning, computer vision, and end-to-end ML delivery.
You Will Be Responsible For
- Collaborating across teams to shape a cohesive digital product and technology strategy enabling product-led transformation and a scalable, reusable, modular, cost-effective digital ecosystem.
In Addition, You Will
- Lead end-to-end AI/ML and GenAI projects from problem framing and requirements through data prep, model development, architecture, production deployment, and post-deployment monitoring/optimization.
- Architect and deliver ML solutions for complex, cross-functional projects across business segments.
- Design, build, and deploy agentic AI systems (multi-agent orchestration, tool-use frameworks, RAG, autonomous decision-making workflows).
- Conduct POCs and rapidly prototype ML/GenAI digital products, translating POCs into production-grade solutions.
- Partner with product management, engineering, and business stakeholders to define product vision, develop technical roadmaps, and deliver measurable business value.
- Provide technical leadership and mentorship; conduct code/model reviews and establish best practices.
- Author/review technical proposals, design documents, and reports; contribute to internal/external AI/ML knowledge sharing.
- Continuously evaluate and adopt emerging AI technologies (foundation models, agentic frameworks, multi-modal AI).
- Establish/enforce model governance, responsible AI practices, reproducibility, and documentation.
Requirements / Qualifications
Required:
- PhD (1+ years) or MS (3+ years) hands-on data science experience deploying large-scale ML in cloud (Azure preferred).
- Hands-on expertise in LLMs/Generative AI/agentic AI with frameworks (LangChain/LangGraph/Semantic Kernel/AutoGen/CrewAI or similar).
- Production deployment experience (model serving, APIs, scalability, monitoring).
- Python; ML libraries (PyTorch/TensorFlow/scikit-learn/Hugging Face) and data tools (Spark/SQL/Pandas).
- Depth in agentic AI (multi-agent systems, tool use, planning/reasoning, memory, human-in-the-loop).
- Prompt engineering, LLM fine-tuning, RAG pipelines, embeddings, and generative AI evaluation.
- Strong communication to explain complex concepts to non-technical stakeholders.
Preferred:
- MLOps/DevOps (CI/CD, 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 detection, governance).
- Multi-functional delivery with business adoption/change management.
- Demand/Supply Planning, Manufacturing, or Supply Chain domain experience.
- Real-time/streaming AI or event-driven architectures.
- Publications/patents in AI/ML.
Benefits (if applicable)
- Medical, dental, vision, life insurance, short- and long-term disability, business accident insurance, group legal insurance; retirement plan and 401(k); long-term incentive program; time-off benefits including vacation, sick time, holidays, work/personal/family time, parental leave, and other specified leave types.