Product Analytics is building self‑service tools and operating AI agents that influence product development; agents that monitor product health, surface anomalies, analyze user behavior, and produce the insights product leaders rely on. We are seeking an AI Engineering Lead to own that layer across a team of roughly 35 analysts supporting 60+ products. You will build the shared repositories, standards, context, and evaluation tooling our analysts depend on, and you will define what production means for the team’s AI work. This is an ongoing leadership role that evolves as the field does, reporting directly to the VP, Product Analytics. You will advocate for the data and tooling the team needs, push to get the right sources into the data lake, work with engineering and TR’s central Data and Analytics team, and connect with AI leaders in other product groups so our work compounds with theirs. Within 12 months we expect agents owning whole analytics workstreams, and this role builds the foundation that gets us there. As AI Engineering Lead, Product Analytics, you will be responsible for: Build and maintain the shared assets our analysts build on: the team’s Git repositories, reusable components, context and data‑access standards, and a registry of what exists and who owns it. Build production AI agents yourself, frequently by picking up a tool another analyst prototyped and extending it into something more capable and broadly useful. Own Evaluations and the Definition of Done: Define what production means for the team’s AI work and own the evaluation standard that holds it there. Close Pipeline Gaps: Find the breaks between collecting the right data and shipping the self‑service AI tooling product managers use to understand user behavior in our products. Diagnose where data, context, or infrastructure is missing,



drive the work to close those gaps, and advocate to get the right sources into the data lake. Keep these changes cheap and fast to make so the standards speed builders up. Propose, with conviction, which workstreams should move fully to AI first, and sequence them so early wins build credibility. Governance and Compliance: Navigate TR’s AI governance landscape on the team’s behalf. Help analysts build to TR standards, support compliance where agents touch sensitive data and decisions, and keep governance workable so it does not block shipping. Represent the team in TR‑wide AI conversations, connect with AI leaders in other product groups, and keep the link to TR’s AI transformation program active. Manage the cross‑team dependencies the work runs on, including data lake access and platform infrastructure. Establish how the team watches its own agents once they run in production, so breakage and quality drift get caught early. Give every production tool a transparent owner and a monitored definition of done. You are a fit for the role of AI Engineering Lead if your background includes: Demonstrated personal investment in AI: you actively track developments, experiment with new tools, and build things on your own initiative. Roughly 2+ years of serious hands‑on building with modern AI tooling, with work you can point to. You are fluent across AI assistants, coding in an AI development environment,



and the patterns of agent design, fluent enough to build production‑grade tools and the shared infrastructure other builders rely on. Experience taking someone else’s prototype and generalizing it into reusable infrastructure is a strong signal, and self‑directed projects count as much as anything done on the job. A working knowledge of how to evaluate AI systems: defining success criteria, building evals, and using them to decide what is ready for production. Substantial experience building structure and programs that scale a capability across a whole team: standards, processes, cadences, and documentation. Strong eye for detail and the discipline to specify what success looks like before building and verify it continuously after. The judgment to know when to push the team toward AI and when a workflow is not ready for it, and the credibility to articulate that vision to analysts and product leaders alike. 4+ years driving change across teams and with senior stakeholders, with a track record of getting people to adopt new ways of working. 5+ years in analytics or a closely related data discipline, with working command of a modern stack (e.g. Snowflake, SQL, Python, BI tools such as Power BI or Tableau, Streamlit) sufficient to lead technical work and judge tool quality. Awareness of AI governance and compliance considerations. Project tracking and program coordination experience (e.g., Linear, Azure DevOps, SharePoint) is an asset. Experience in or alongside product teams and SaaS experience are assets. #LI-ES1 Replacement: This position is open due to an existing vacancy to support our evolving business needs. Hybrid Work Model: We’ve adopted a flexible hybrid working environment (2‑3 days a week in the office depe

📌 AI Engineering Lead, Product Analytics (Canada)
🏢 Thomson Reuters
📍 Canada