Data Science Lead

Employment Status: Full-time
- Permanent

Location:
Edmonton, Edmonton Research Park Facility Work Model: Hybrid About the Opportunity The Data Science Lead is an opportunity to be part of a professional, purpose-driven team that values collaboration, innovation, accountability, and continuous improvement. Most importantly, your work will contribute to meaningful outcomes that make a difference for Albertans and help shape the future of our province.

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/