Technical Product Manager

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.

Communicate roadmap status, risks, and trade-offs clearly to senior stakeholders on both the client and ShyftLabs sides.

Contribute to how we do product here, our discovery practices, prioritization frameworks, and delivery rituals are still being shaped, and we want your input on them.

3–6 years of product management experience, including at least 2 years shipping data, analytics, platform, or AI-powered products.

A track record of taking products from problem statement to production, not just maintaining an existing backlog.

Strong technical fluency: you can hold your own in an architecture discussion, read a data model, reason about APIs and integrations, and understand why a pipeline decision has downstream consequences.

Comfort working with enterprise stakeholders, including the ability to push back constructively on a request that isn't the right thing to build.

Analytical rigour, you use data to frame decisions and you're honest about what the data doesn't tell you.

Experience with Agile delivery and modern product tooling (Jira, Confluence, Figma, or equivalents).

Excellent written communication. Much of the job is writing things down clearly enough that people can act on them.

Bachelor's degree in Computer Science, Engineering, Business, or a related field, or equivalent practical experience

Background in retail, commerce media, or retail media networks.

Familiarity with LLM-based products: RAG, prompt orchestration, tool calling, evaluation frameworks, and guardrails.

Working knowledge of cloud data platforms such as Databricks, Snowflake, or BigQuery, and how they shape product constraints.

SQL skills sufficient to answer your own questions without waiting on someone else.

Prior experience in a startup or client-facing product environment.