Remote Technical Product Owner - AI Delivery

at Insight Global in Westbrook, Maine, United States

Job Description

Job Description

We are seeking an experienced Technical Product Owner to join the Data and AI Center of Excellence (DAICOE) at IDEXX to own 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 productionization 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.

We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to HR@insightglobal.com.To learn more about how we collect, keep, and process your private information, please review Insight Global’s Workforce Privacy Policy: https://insightglobal.com/workforce-privacy-policy/.

Skills and Requirements

· Proven experience as a Product Owner or Product Manager for AI/ML or data science product delivery.

· Direct experience working with cross-functional teams of data scientists and ML engineers in a production ML environment.

· Ability to read and interpret model evaluation results, experiment logs, and ML pipeline outputs as an informed decision-maker – not necessarily as a practitioner.

· Demonstrated ability to write technically specific acceptance criteria for both DS model work and MLE productionization work.

· Experience distinguishing and sequencing research/experimentation work from engineering/production work in a backlog.

· Experience with the DS-to-MLE model productionization handoff: what constitutes a complete handoff, what risks arise from incomplete ones.

· Strong prioritization and trade-off decision-making skills in an environment of high technical uncertainty.

· Ability to work effectively across multiple tasks and teams while meeting aggressive timelines.

· Outstanding written and verbal communication skills; able to explain ML model trade-offs and delivery risks to non-technical stakeholders.