As a Data Scientist, you will be a valued member of the AI Centre of Excellence (COE) within the Office of the Chief Technology Officer at EY Canada. You will work closely with senior team members to support advanced AI research and development, contributing to the construction and integration of intelligent solutions across transformative projects. This role is ideal for individuals eager to start their careers in AI and machine learning, build robust ML pipelines, and apply cutting-edge research in enterprise-scale applications. You will help deliver impactful AI use cases, including large language model (LLM) applications, and play a part in shaping the future of artificial intelligence at EY.
Your key responsibilities
- Develop & Deploy AI Solutions: Assist in designing, building, and optimizing machine learning pipelines for a range of enterprise and research applications, leveraging Python, PyTorch/TensorFlow, and cloud platforms under guidance from senior staff.
- AI Model Implementation: Support the implementation, fine-tuning, and evaluation of AI models, including large language model (LLM) solutions for use cases in generative AI and natural language processing.
- Model Evaluation & ML Ops: Contribute to the end-to-end model lifecycle, from ideation and training to validation, deployment, and monitoring, while learning ML Ops best practices for operational excellence.
- Collaboration & Stakeholder Engagement: Work closely with data scientists, engineers, and business stakeholders to deliver scalable and ethical solutions as part of a cross-functional team.
- Documentation & Communication: Clearly document technical results, methodologies, and insights; communicate findings to both technical and non-technical audiences.
- Continuous Learning: Proactively 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: Support EY’s standards for ethical, equitable, and transparent AI, ensuring compliance with legal and security guidelines.
Skills and attributes for success
- Education: Bachelor’s in Computer Science, Data Science, Statistics, Artificial Intelligence, Machine Learning, or a closely related field.
- Experience: 1–3 years of hands-on experience delivering AI/ML solutions in academic, research, or enterprise settings.
- Technical Proficiency: Proficient in Python programming and familiar with deep learning frameworks such as PyTorch and/or TensorFlow; strong skills in data wrangling, feature engineering, and ML pipeline construction and 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.
- AI Model Deployment: Exposure to deploying models on cloud platforms (e.g., Azure ML or similar).
- Collaboration: Proven ability to work effectively in team-based, cross-functional environments.
- Communication: Strong written and verbal communication skills; ability to articulate technical concepts to diverse audiences and contribute to internal knowledge sharing.
- Problem-Solving: Analytical thinker passionate about finding innovative solutions to technical challenges.