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# Technical Product Owner

- **Company:** Compunnel Inc.
- **Location:** Westbrook, ME, United States
- **Experience:** Experienced
- **Salary:** $114,400.0 - $128,960.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Machine Learning, Scrum Methodology, Information Technology
- **Published:** August 9, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=b2bfca7f761c0925

## About the Role

* Bachelor's degree or above in Computer Science, Data Science, Statistics, or related field; Master's degree preferred.
 * 2-3 years of experience in roles as Product Owner, Product Manager, or technical delivery lead for AI/ML or data science products.
 * Demonstrated track record of shipping ML models into production across the full lifecycle: from problem framing through monitoring.

## Description

We are seeking an experienced Technical Product Owner to join the Data and AI Center of Excellence (DAICOE) at the delivery of AI/ML models for a defined product cluster. This role is the primary bridge between product-level priorities set by the Product Manager and the technical work of a cross-functional team of data scientists, ML engineers, and data engineers. You will own the AI Layer backlog for your product cluster - managing model development workstreams, making experiment scope and continuation decisions, and ensuring a clean DS-to-MLE product ionization handoff. You will partner closely with a Tech Leads (DS and MLE) who own technical feasibility and other POs who own external technical dependency resolution., * Own and maintain a prioritized AI Layer backlog for the assigned product cluster, with DS and MLE work represented as distinct, sequenced backlog items.

 * Translate product-level priorities from the Product Manager into AI Layer workstreams with clear, technically specific acceptance criteria for both DS and MLE work.
 * Write acceptance criteria for DS work (model performance thresholds, evaluation methodology, holdout set specification, model card completeness) and MLE work (serving latency SLOs, monitoring requirements, rollback procedures) separately.
 * Own the DS-to-MLE Handoff Review ceremony: ensure model readiness criteria - including eval documentation, serving requirements, and monitoring criteria - are fully met before MLE operationalization work enters a sprint.
 * Partner with the Tech Lead at every backlog refinement to validate feasibility, surface technical risks, and confirm story scope and sizing before sprint commitment.
 * Make sprint-level trade-off decisions - scope, quality threshold, experiment continuation or termination - with authority and appropriate speed.
 * Facilitate sprint planning, backlog refinement, sprint demo, and retrospective ceremonies for the assigned team.
 * Surface external dependency blockers to the appropriate owner immediately.
 * Shield the team from unplanned work and context-switching by enforcing backlog discipline and managing stakeholder expectations.
 * Continuously improve team leverage through AI, agents, and workflow automation.
 * Automate routine delivery-management activities including backlog refinement, reporting, dependency tracking, and handoff validation where appropriate.
 * Measure and report efficiency gains from AI-enabled delivery practices.


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