EY Senior Data Scientist AI Centre Excellence 

Canada, Ontario, Toronto 

674488435

Your key responsibilities

• Develop & Deploy AI Solutions: Design, build, and optimize advanced machine learning pipelines for a variety of enterprise and research applications, leveraging best-in-class programming, frameworks, and cloud platforms.
• LLM Application Engineering: Implement, fine-tune, and evaluate large language model (LLM) solutions for diverse use cases, including generative AI, natural language processing, and retrieval-augmented generation.
• Research Integration: Translate cutting-edge academic research into practical, scalable AI solutions aligned with EY’s business objectives and innovation agenda.
• Model Evaluation & ML Ops: Support the end-to-end model lifecycle from ideation, training, and validation through deployment and monitoring, applying ML Ops best practices for operational excellence.
• Collaboration & Stakeholder Engagement: Work closely with data scientists, engineers, business stakeholders, and product teams to deliver robust, scalable, and ethical solutions.
• Documentation & Communication: Clearly communicate technical results, methodologies, and insights to both technical and non-technical audiences; contribute to research publications and internal knowledge sharing.
• Continuous Learning: Stay current with the latest advancements in AI/ML, tools, and frameworks; actively contribute to a culture of innovation and excellence within the AI COE.
• Champion Responsible AI: Uphold EY’s standards for ethical, equitable, and transparent AI, ensuring compliance with legal and security guidelines.

Skills and attributes for success

• Education: PhD or Master’s degree in Data Science, Computer Science, Artificial Intelligence, Machine Learning or a closely related field.
• Experience: 4–6 years of hands-on experience delivering impactful AI/ML solutions, ideally in enterprise or research settings.
• Technical Proficiency: Proficient with Python and deep learning frameworks (PyTorch and/or TensorFlow); expertise in data wrangling, feature engineering, and ML pipeline automation.
• GenAI & LLM: Experience with large language model architectures, generative AI use cases, and model fine-tuning and deployment.
• ML Ops & Cloud: Familiarity with ML lifecycle management, ML Ops concepts, and deployment on Azure ML or equivalent cloud platforms.
• Collaboration: Strong team orientation with the ability to work effectively in diverse, cross-functional environments.
• Communication: Excellent written and verbal communication skills; able to articulate complex technical concepts to diverse audiences and contribute to research outputs.
• Problem-Solving: Analytical thinker with a track record of innovative solutions to complex technical challenges.