Lead Data Scientist, AI Centre of Excellence

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As a Lead Data Scientist of the AI Centre of Excellence at the Office of the CTO, you will play a pivotal role in shaping the future of artificial intelligence at EY Canada. In this position, you will guide the AI strategy, lead the design and implementation of advanced AI models, and drive innovation across transformative projects. Your leadership will directly influence real‑world deployment of cutting‑edge solutions in areas such as AI, generative AI and natural language processing, and other emerging technologies.

If you are passionate about advancing the frontiers of AI, have a proven record of groundbreaking research and impactful innovation, and thrive in a collaborative environment, we invite you to help build the next era of intelligent solutions with us.

• Lead AI Strategy:
  Define and execute the overarching AI strategy in alignment with EY’s business goals, collaborating with cross‑functional leaders and technology stakeholders.
• Leadership Responsibilities:
  Inspire and lead a team of data scientists and engineers, fostering collaboration and innovation. Drive the vision for AI initiatives and ensure alignment with organizational objectives. Facilitate knowledge sharing and continuous learning within the team.
• Oversee Model Architecture & Innovation:
  Architect, evaluate, and refine complex models, including transformers, LLMs (Large Language Models), and retrieval‑augmented generation (RAG) solutions for diverse enterprise use cases.
• Long‑Term Innovation Focus:
  Drive long‑range planning for AI capabilities, champion emerging technologies, and identify opportunities for disruptive innovation in AI/ML applications across business verticals.
• Research Leadership:
  Publish and drive high‑impact research papers and thought leadership in journals and conferences; represent EY at industry forums.
• Model Lifecycle & ML Ops:
  Lead end‑to‑end model lifecycle management, from ideation and training to deployment and monitoring, leveraging ML Ops best practices and Azure ML.
• Stakeholder
  
  Collaboration:
  
  Partner with business, engineering, and product teams to translate visionary AI concepts into robust, scalable solutions delivering measurable value.
• Champion Responsible AI:
  Ensure AI systems adhere to ethical, legal, and security guidelines; promote equity, transparency, and accessibility in all initiatives.

• Education:
  
  PhD or Master’s degree in Artificial Intelligence, Machine Learning, Data Science, Computer Science, or a closely related field.
• Experience:
  
  7–10 years of progressive experience delivering impactful AI/ML solutions, with a proven track record in architecting and scaling NLP, AI and GenAI systems.
• GenAI & LLMs:
  Deep expertise with generative AI models, transformer architectures, LLM fine‑tuning, and deployment of RAG pipelines.
• ML Ops & Cloud:
  Hands‑on experience with ML lifecycle management, ML Ops frameworks, and Azure ML (or equivalent cloud ML platforms).
• Technical Proficiency:
  Strong programming skills (Python or similar), deep learning frameworks (PyTorch, Tensor Flow, Hugging Face), and data engineering for large‑scale modeling.
• Innovation & Research:
  Proven ability to publish research, file…