[Hiring] Technical Product Owner @Artech

Role Description

We are seeking an experienced Technical Product Owner to join the **** at **** 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 Tech Leads (DS and MLE) who own technical feasibility and other POs who own external technical dependency resolution.

What you will do:

• 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.

Qualifications

• 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.

Requirements

• 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.

Additional Desirable Experience

• Background in data science, machine learning, or a quantitative field.
• Familiarity with ML experiment tracking and model lifecycle tooling.
• Experience with real-time inference products and latency-sensitive ML serving requirements.
• Prior experience owning or managing an ML platform or model serving infrastructure.
• Experience with Databricks or equivalent cloud ML platform.