Principal Data Scientist

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Principal Data Scientist, Agentic AI Technical Lead based in Canada.

This is a principal-level technical leadership role responsible for setting the direction of a large-scale agentic AI program. You’ll provide technical oversight across multiple AI workstreams, from problem framing and architecture through evaluation, deployment, and production operations. The role combines deep hands-on expertise in data science and AI/ML with program-wide architectural leadership and strategic decision-making. You’ll partner with executives and senior stakeholders to shape the AI roadmap and make high-impact decisions around technology, risk, quality, and delivery. You’ll establish standards for agentic systems, model evaluation, MLOps, reliability, security, and responsible AI across a large delivery organization. The environment is fast-moving and highly collaborative, with production AI expected to move from concept to deployment in approximately 30–45 days.

• Provide technical oversight across all agentic AI workstreams, guiding initiatives from problem definition and architecture through evaluation, deployment, and ongoing operation.
• Define the technical strategy and reference architecture for agentic AI systems, including multi-agent orchestration, tool and function calling, RAG, vector databases, embeddings, and streaming LLM responses.
• Coordinate technical delivery across multiple teams and workstreams, identifying dependencies, resolving blockers, managing technical risk, and maintaining quality and velocity.
• Establish standards for model development, experimentation, evaluation, and the transition from research concepts to reliable production systems.
• Design and champion rigorous evaluation frameworks covering model and agent quality, safety, hallucination, cost, latency, and other meaningful performance indicators.
• Guide the development of scalable ML platforms, pipelines, event-driven architectures, and workflow orchestration systems supporting asynchronous AI operations.
• Ensure deployed AI systems meet high standards for reliability, security, scalability, observability, monitoring, and production debugging.
• Serve as the senior technical representative for the AI/ML program with executive and client stakeholders, communicating progress, risks, trade-offs, and technical decisions clearly.
• Partner with executive leadership to define and evolve the AI roadmap, contributing as a strategic technical peer on high-impact decisions.
• Translate complex AI and data science concepts into actionable decisions for executives, engineers, product teams, and business stakeholders.
• Align data science, engineering, product, and business teams around shared priorities, technical standards, and measurable outcomes.
• Lead and influence a large, multi-team delivery organization while establishing expectations for technical excellence across workstreams.
• Review technical work across teams, provide direct and constructive feedback, and raise the quality of architecture, implementation, and delivery.
• Mentor technical leads and senior practitioners, helping develop the next generation of AI and technical leaders.
• Own high-stakes technical decisions that affect the broader program, balancing innovation, delivery speed, reliability, and risk.
• Continuously evolve the technical vision and define how agentic AI engineering and data science practices should mature across the program.
• Use modern AI-assisted development tools and workflows to improve productivity, engineering quality, and delivery speed.

• 12+ years of professional experience in data science and AI/ML, with a proven record of taking AI systems from research or experimentation into production at enterprise scale.
• Demonstrated experience leading technical delivery across multiple concurrent workstreams, teams, or large AI programs, with influence extending beyond a single engineering team.
• Proven ability to manage technical dependencies, risks, architectural decisions, and delivery quality across complex multi-team initiatives.
• Exceptional stakeholder management and communication skills, including credibility with executives, client leadership, engineering teams, and business stakeholders.
• Deep hands-on experience with LLM and agentic AI systems, including prompt engineering, tool and function calling, multi-agent orchestration, RAG architectures, vector databases, embeddings, and streaming LLM responses.
• Strong expertise in model evaluation, experimentation design, applied statistics, and evaluation methodologies specifically suited to generative and agentic AI systems.
• Advanced Python skills and strong proficiency across modern data science and machine learning technologies.
• Extensive MLOps and AI infrastructure experience, including model versioning, monitoring, deployment automation, reproducibility, and production operations.
• Strong software engineering fundamentals, including system design, API design, code quality, testing, and maintainable architecture.
• Experience with distributed systems, event-driven architectures, and workflow orchestration technologies.
• In-depth AWS experience, particularly with AWS GenAI services such as Amazon Bedrock, alongside working knowledge of other cloud platforms.
• Familiarity with SQL and NoSQL databases and scalable data-storage and access patterns.
• Working knowledge of AI governance, responsible AI, safety, and compliance considerations for production systems.
• Strong ability to operate in ambiguous environments, make decisions with incomplete information, and balance technical trade-offs.