Lead Data Scientist

Johnson & Johnson Johnson & Johnson · Pharma · Toronto, ON +1

Lead Data Scientist role focused on designing, developing, and deploying production-grade AI solutions, particularly agentic AI systems, LLMs, and Generative AI, within the MedTech Supply Chain Digital organization at Johnson & Johnson. The role involves end-to-end project leadership, from problem framing to deployment and monitoring, with a strong emphasis on building scalable and reusable solutions.

What you'd actually do

  1. Lead end-to-end AI/ML and GenAI projects, from business problem framing and requirements gathering through data preparation, model development, solution architecture, production deployment, and post-production monitoring and optimization
  2. Architect and deliver comprehensive ML-based solutions for highly complex, cross-functional projects that address diverse challenges and opportunities across J&J's business segments
  3. Design, build, and deploy agentic AI systems, including multi-agent orchestration, tool-use frameworks, retrieval-augmented generation (RAG), and autonomous decision-making workflows
  4. Conduct proof-of-concepts (POCs) and rapidly prototype ML/GenAI-based digital products aligned with business priorities across various segments and functions, translating POCs into production-grade solutions
  5. Collaborate closely with product management, engineering, and business partners to define product vision, develop technical roadmaps, and ensure AI solutions deliver measurable business value

Skills

Required

  • PhD with 1+ years or MS with 3+ years of proven hands-on experience in data science, building and deploying large-scale ML models and systems in cloud environments (Azure preferred)
  • Demonstrated expertise in LLMs, Generative AI, and agentic AI including hands on experience with frameworks such as LangChain, LangGraph, Semantic Kernel, AutoGen, CrewAI, or similar orchestration tools
  • Proven track record of deploying AI/ML solutions to production including model serving, API development, scalability considerations, and monitoring
  • Strong programming skills in Python; proficiency with ML libraries (such as PyTorch, TensorFlow, scikit-learn, Hugging Face, etc.) and data processing tools (Spark, SQL, Pandas)
  • Depth and breadth in state-of-the-art agentic AI technologies, multi-agent systems, tool use, planning and reasoning architectures, memory management, and human-in-the-loop patterns
  • Experience with prompt engineering, LLM fine-tuning, RAG pipelines, embedding strategies, and evaluation frameworks for generative AI solutions
  • Excellent communication, interpersonal, and written skills ability to translate complex technical concepts

What the JD emphasized

  • strong hands-on expertise in Generative AI, LLMs, agentic AI systems
  • production-grade AI solutions
  • rapid prototyping, iterative experimentation
  • AI/ML product strategy
  • end-to-end AI/ML and GenAI projects
  • production deployment
  • agentic AI systems
  • retrieval-augmented generation (RAG)
  • deploying AI/ML solutions to production
  • state-of-the-art agentic AI technologies
  • evaluation frameworks for generative AI solutions

Other signals

  • Generative AI
  • LLMs
  • agentic AI systems
  • ML solution delivery
  • production deployment