Technical Product Manager

ABOUT SHYFTLABS

ShyftLabs is a data and AI company. Since 2020 we've partnered with Fortune 500 organizations across retail, healthcare, finance, energy, and the public sector to turn messy enterprise data into systems that actually move the business. Our team of 200+ builds privacy-first AI, modern data infrastructure, and analytics products that have unlocked hundreds of millions in measurable value for our clients.

We're scaling quickly, and we hire people who'd rather ship something real than write another deck about it.

THE OPPORTUNITY

We're looking for a Technical Product Manager to own a portfolio of data and AI products and take Product from problem definition through launch and iteration. You'll sit between enterprise clients, engineering, and data science, translating ambiguous business problems into clear product bets, then driving them to delivery.

This is a hands-on role at a company where the products are technical and the customers are demanding. You'll spend as much time in discovery conversations with client stakeholders as you do in backlog refinement with engineers. If you like owning outcomes rather than tickets, and you're comfortable being the person accountable when a launch either works or doesn't, this is a good fit.

This role is based in Toronto and follows a hybrid schedule, with three days per week in our downtown office.

What You'll Be Doing

• Own the roadmap for one or more data and AI product lines — you set priorities, you defend the trade-offs, and you're accountable for the results.

• Run discovery directly with enterprise clients and internal stakeholders to identify high-value problems worth solving, and separate them from the noise.

• Write clear, decision-ready product requirements: user stories, acceptance criteria, data contracts, edge cases, and what “done” actually means.

• Partner day-to-day with engineering, data engineering, and design to sequence delivery, unblock decisions quickly, and keep scope honest.

• Define success metrics up front, adoption, data quality, time-to-insight, model reliability, cost per outcome, and instrument products so those metrics are observable after launch.

• Lead sprint ceremonies alongside engineering leads and keep delivery predictable without turning the team into a ticket factory.

• Turn client feedback, usage data, and support signals into a prioritized backlog of improvements rather than a list of one-off requests.

• Manage launches end to end: rollout plans, enablement material, internal training, and post-launch review.