Employment Status: Full-time - Permanent
Location: Edmonton, Edmonton Research Park Facility
Work Model: Hybrid
Key responsibilities in the role of
Data Science Lead include:
AI/ML Use-Case Delivery (End-to-End)- Partner with business and program teams to identify, scope, and prioritize high-value use cases, translating business objectives into measurable ML/AI outcomes.
- Lead the full lifecycle - problem framing, data preparation, modelling, validation, and deployment - for the AI/ML use cases they lead, across classical ML and generative/LLM applications.
- Apply MLOps/LLMOps practices for their own solutions and partner with platform and engineering teams for production hardening, CI/CD, and monitoring.
- Measure and communicate the accuracy, business value, and risk of deployed solutions.
Generative & Agentic AI Development and Deployment- Design, build, and deploy agentic AI solutions on Microsoft Copilot (e.g., Copilot Studio / Azure AI Foundry) and Anthropic Claude (Claude API / Claude Agent SDK), using tool/function calling and Model Context Protocol (MCP).
- Implement closed-loop LLM systems - retrieval-augmented generation (RAG), evaluation loops, guardrails, and human-in-the-loop feedback - operated within controlled or private environments.
- Recommend standards for prompt design, retrieval quality, evaluation, and guardrails, and apply them to their solutions.
- Configure agent triggers, permissions, and human oversight consistent with standards set with Cybersecurity and Data Governance.
Model Development & Fine-Tuning on Internal Compute- Train, fine-tune, and evaluate models - including open-weight LLMs - on internal / private compute where data residency or privacy requires it, using parameter-efficient methods (e.g., LoRA/QLoRA).
- Work with Infrastructure and Platform teams to provision and right-size the compute required, rather than owning the GPU environment.
- Follow the organization's data-classification policy and work with Data Governance on training-data access and de-identification.
AI Literacy & Enablement- Contribute to organizational AI literacy through role-based enablement sessions, executive briefings, and a data-science community of practice, in partnership with People/HR and Learning & Development.
- Promote approved-tool boundaries and safe data-handling practices.
- Mentor analysts and technical staff and build shared tooling and reusable assets that raise data-science maturity.
- Represent the data-science perspective in cross-functional discussions.
Responsible AI & Continuous Improvement- Apply responsible-AI practices (fairness, transparency, documentation, human oversight) and support algorithmic-impact assessments led by the governance and privacy function.
- Monitor emerging models, tools, and methods and recommend improvements.
- Provide technical input to evaluations of data science, ML, and AI tools and platforms.
- Collaborate with Data Governance, Cybersecurity, Enterprise Architecture, Privacy, and Infrastructure to keep solutions secure, compliant, and well-governed.
What You Need to Thrive in This Role- Diploma in Computer Science, Data Science, Statistics, Mathematics, Engineering, Information Systems, or a related discipline.
- 7 years of progressively responsible experience in data science, machine learning, or applied AI, including experience taking solutions into production.
- Demonstrated end-to-end delivery of AI/ML use cases (from discovery through production) with measurable business value required
- Demonstrated experience developing and deploying agentic AI using Microsoft Copilot and Anthropic Claude (including tool/function calling and MCP) required.
- Demonstrated experience implementing closed-loop LLM systems (RAG, evaluation loops, guardrails, and human-in-the-loop feedback) required.
- Demonstrated experience fine-tuning models on internal / private compute (e.g., LoRA/QLoRA) where data residency requires it required.
- Demonstrated experience building AI literacy in an organization (role-based enablement, executive briefings, or a community of practice) required
- Experience in public-sector, quasi-government, or research environments considered an asset.
- Certifications in cloud or AI/ML platforms (e.g., Microsoft Azure AI Engineer Associate, Azure Data Scientist Associate, Databricks) considered an asset.
- Credentials related to responsible AI or model governance considered an asset.
- Strong applied knowledge of machine learning and statistical methods and of the generative-AI stack (LLMs, RAG, embeddings and vector databases, agents, and MCP).
- Working knowledge of MLOps/LLMOps concepts (deployment, monitoring, evaluation) sufficient to collaborate with platform and engineering teams and hand off cleanly.
- Working knowledge of model fine-tuning, including open-weight models and parameter-efficient methods (LoRA/QLoRA).
- Understanding of responsible AI, human oversight, and algorithmic-impact assessment, and awareness of privacy, security, and data-classification requirements in a public-sector environment.
- Ability to translate ambiguous business problems into well-scoped, deployable AI/ML solutions and to measure their value.
- Effective communication and facilitation skills; ability to influence without authority and translate technical concepts for non-technical audiences.
What We OfferAt
Alberta Innovates we believe our people are our greatest asset. That's why we offer a comprehensive total rewards package that supports your career growth, wellbeing, and work-life balance.
- Competitive compensation aligned with our internal career framework and pay structures
- Comprehensive health, dental, and pension benefits to support you and your family
- Professional development and learning opportunities to help you grow and advance your career
- A collaborative and inclusive workplace where diverse perspectives are valued and every contribution matters
- Flexible work arrangements, where operationally appropriate, along with vacation and earned time off to rest, recharge and help you bring your best self to work.
The minimum starting salary for this position is $88,600 annually. Placement within the salary range will be based on the successful candidate's qualifications, relevant experience, and internal equity.
How to ApplyInterested and qualified applicants are encouraged to submit their resume and cover letter prior to the application deadline of
October 16, 2026.The successful candidate will be required to undergo a security clearance and provide credible references.
