Hire Nearshore Agile Product Owners | TeamStation AI

In an Agile team building AI products, the Product Owner plays a critical role. They are responsible for translating the high-level product vision into a detailed, prioritized backlog of user stories and technical tasks that a data science and engineering team can execute on. Our vetting process finds Product Owners who excel at this, with a special focus on their ability to manage the unique challenges of an AI development lifecycle.

Is your backlog for AI projects vague and un-actionable?

The Problem

It's difficult to break down a complex AI goal (e.g., 'improve recommendation accuracy') into small, incremental stories that a team can work on. This leads to long, risky research spikes and a lack of demonstrable progress.

The TeamStation AI Solution

We provide Product Owners who are skilled at creating a well-defined backlog for AI projects. They are experts at writing user stories that are independent, negotiable, valuable, estimable, small, and testable (INVEST), even for experimental ML tasks.

Proof: A clear, actionable backlog for AI/ML projects.
Is your team struggling with the uncertainty of research-heavy sprints?

The Problem

AI development is often more experimental than traditional software development. Sprints can easily fail to deliver a 'done' increment, leading to frustration and a sense of unpredictability.

The TeamStation AI Solution

Our Product Owners are experienced in managing Agile for AI. They know how to structure sprints that balance research spikes with engineering tasks, and how to define a 'valuable increment' that might be a learning outcome rather than a shippable feature, keeping the team motivated and productive.

Proof: Improved predictability and morale in AI-focused sprints.

How We Measure Seniority: From L1 to L4 Certified Expert

We don't just match keywords; we measure cognitive ability. Our Axiom Cortex™ engine evaluates every candidate against a 44-point psychometric and technical framework to precisely map their seniority and predict their success on your team. This data-driven approach allows for transparent, value-based pricing.

L1 Proficient

Guided Contributor

Contributes on component-level tasks within the Product Owner domain. Foundational knowledge and learning agility are validated.

Evaluation Focus

Axiom Cortex™ validates core competencies via correctness, method clarity, and fluency scoring. We ensure they can reliably execute assigned tasks.

$20 /hour

$3,460/mo · $41,520/yr

± $5 USD

L2 Mid-Level

Independent Feature Owner

Independently ships features and services in the Product Owner space, handling ambiguity with minimal supervision.

Evaluation Focus

We assess their mental model accuracy and problem-solving via composite scores and role-level normalization. They can own features end-to-end.

$30 / hour

$5,190/mo · $62,280/yr

± $5 USD

L3 Senior

Leads Complex Projects

Leads cross-component projects, raises standards, and provides mentorship within the Product Owner discipline.

Evaluation Focus

Axiom Cortex™ measures their system design skills and architectural instinct specific to the Product Owner domain via trait synthesis and semantic alignment scoring. They are force-multipliers.

$40 / hour

$6,920/mo · $83,040/yr

± $5 USD

L4 Expert

Org-Level Architect

Sets architecture and technical strategy for Product Owner across teams, solving your most complex business problems.

Evaluation Focus

We validate their ability to make critical trade-offs related to the Product Owner domain via utility-optimized decision gates and multi-objective analysis. They drive innovation at an organizational level.

$50 / hour

$8,650/mo · $103,800/yr

± $10 USD

Pricing estimates are calculated using the U.S. standard of 173 workable hours per month, which represents the realistic full-time workload after adjusting for federal holidays, paid time off (PTO), and sick leave.

Core Competencies We Validate for Product Owner

Agile for AI/ML
Backlog Management for Research & Engineering
Writing User Stories for Data Science
Sprint Goal Definition for Experimental Work
Stakeholder Communication on Probabilistic Outcomes

Our Technical Analysis for Product Owner

Our evaluation for Product Owners in an AI context focuses on their ability to manage uncertainty. Candidates are given a project brief for a new machine learning feature and are required to create a story-mapped backlog for the first three sprints. We assess their ability to break down the work into a mix of data exploration, model training, and engineering tasks. They must be able to define clear acceptance criteria for a data science 'experiment' and explain how they would communicate progress to stakeholders when the outcome is uncertain.

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