Data Scientist II

Role overview

Pay details

• Posted base pay range is $81,600–$115,200 CAD. The description does not specify the compensation cadence.

Key accountabilities

• Partner with Finance stakeholders to understand challenges, identify opportunities, and turn questions into analytics and AI solutions.
• Identify Finance use cases for generative AI, Copilot, and agentic solutions.
• Design, prototype, test, and operationalize AI solutions using approved platforms and trusted data.
• Develop reusable agent capabilities, prompts, instructions, context, grounding, and workflow integrations.
• Evaluate quality, accuracy, reliability, business relevance, and user adoption.
• Prepare semantic models, metadata, and data foundations for AI and Copilot experiences.
• Coordinate with business, data, technology, risk, and governance stakeholders through controlled implementation.
• Monitor deployed solutions, investigate issues, incorporate feedback, and improve performance.
• Analyze complex datasets and communicate insights and recommendations using visualization and storytelling.
• Support and modernize analytics and Alteryx workflows; collaborate across teams and share knowledge.

Required qualifications and skills

• At least 3 years of relevant data science, advanced analytics, AI engineering, machine learning, or related experience.
• Hands-on experience developing generative AI, Copilot, or agentic business solutions.
• Strong Python or PySpark and SQL skills.
• Experience with prompt engineering, context design, retrieval or grounding, and evaluation of generated outputs.
• Experience integrating AI with enterprise data, APIs, tools, workflows, or analytics products.
• Understanding of responsible AI, privacy, security, model risk, human oversight, and controlled deployment.
• Experience analyzing complex data and communicating actionable recommendations.
• Strong business acumen, experimentation, problem-solving, data storytelling, communication, and stakeholder-management skills.
• Able to own assignments from problem definition through implementation, validation, operationalization, and handoff.
• Undergraduate degree in computer science, engineering, mathematics, statistics, finance, or a related field, or equivalent practical experience.

Additional assets

• Experience with Databricks, Microsoft Copilot, Copilot Studio, Power BI, semantic models, or comparable AI and data platforms is an asset.
• Finance, accounting, banking, or broader financial-services experience is an asset.