AI Product Analyst

SCIGON; Chicago (IL); On-site; USD 120,000 - 160,000; Full time

Job summary

SCIGON is seeking a Product Owner for the AI Engineering delivery pipeline in the United States. This role leads solution development with close partnership to engineering teams and is not a hands-on coding position.

You will manage intake, define success metrics, and set acceptance thresholds for AI/LLM-enabled solutions, while partnering with executives and business stakeholders to ensure measurable value realization.

Qualifications

• Three or more years of experience in Product Management, Product Ownership, and Product Analysis.
• Ownership of at least one AI or LLM-enabled product successfully deployed to production.
• Experience creating business cases with baseline, success metric, and measured outcomes.
• Executive and operational stakeholder management including intake management and opportunity scoring.
• Familiarity with AI governance frameworks and cross-functional collaboration with engineering and business teams.

Responsibilities

• Managing intake, qualification, prioritization, and opportunity assessment for AI initiatives.
• Developing business cases and defining value measures, baselines, success metrics, and attribution.
• Defining requirements and acceptance criteria for AI/LLM-enabled solutions.
• Authoring evaluation specs, curating golden datasets, and establishing production readiness thresholds.
• Leading user validation, adoption tracking, and value realization after deployment.
• Monitoring portfolio health: ownership, lifecycle, versioning, deprecation, retirement.
• Managing governance for AI skills, plug-ins, and MCP assets translating security and compliance into product requirements.
• Building partnerships with business leaders, operational stakeholders, and executives with disciplined intake.

Skills

Product Management

Product Ownership

Product Analysis

Job description

This role sits within a centralized AI Engineering and Automation Center of Excellence responsible for delivering enterprise-scale AI and automation solutions across a large, distributed North American organization.

The AI work is real, deployed, and actively supporting business operations. This is not a pilot program or innovation lab.

Current capabilities include:

• Reusable AI skills and plug-ins packaged into governed applications used across multiple business functions
• Retrieval-Augmented Generation (RAG), embeddings, and multi-step workflows operating on governed enterprise data
• A governed AI asset marketplace and Model Context Protocol (MCP) catalog covering intake, publishing, versioning, lifecycle management, and maintenance
• Enterprise-grade evaluation frameworks, golden-set testing, guardrails, monitoring, and logging designed to ensure reliable AI outputs at scale

The organization also operates a mature automation ecosystem alongside its AI initiatives, including:

• Approximately 125 unattended production robots
• More than 200 production automations
• More than 19,000 automation jobs executed every 30 days
• Platforms including UiPath and Microsoft Power Platform

For projects requiring significant engineering investment, such as new backend services, infrastructure implementation, or highly complex integrations, a dedicated Enterprise Engineering organization partners with the AI Engineering team to build and deliver solutions. Once completed, those assets are cataloged, governed, and managed through established operational processes.

What This Role Actually Is

This role serves as the Product Owner for the AI Engineering delivery pipeline.

The successful candidate will lead and direct solution development efforts while partnering closely with engineering teams. This is not a hands‑on software engineering role, and the individual in this position will not be writing production code.

Key responsibilities include:

• Managing intake, qualification, prioritization, and opportunity assessment for AI initiatives
• Developing business cases and defining value measurement strategies, including establishing baselines, success metrics, attribution methodologies, and measurable outcomes
• Defining requirements and acceptance criteria for AI and LLM-enabled solutions
• Authoring evaluation specifications, curating golden datasets, and establishing acceptance thresholds used to determine production readiness
• Leading user validation activities, adoption tracking, and value realization assessments after deployment
• Monitoring portfolio health across AI assets, including ownership, lifecycle management, versioning, deprecation, and retirement planning
• Managing governance processes for AI skills, plug-ins, and MCP catalog assets, translating security, privacy, compliance, and regulatory requirements into actionable product requirements
• Building strong partnerships with business leaders, operational stakeholders, and executive teams while maintaining disciplined intake and prioritization processes

Engineering teams own:

• Evaluation harness implementation
• Monitoring instrumentation

This role owns:

• Defining what success looks like
• Defining evaluation criteria
• Defining acceptance thresholds
• Determining production readiness
• Making the final product recommendation for release

Candidates who view evaluation criteria, testing strategy, or acceptance definitions as purely engineering responsibilities are unlikely to be successful in this position.

Must Have

The following are required screening criteria.

Candidates who do not meet all requirements should not be considered a fit.

1. Product Management Experience

Three or more years of experience in:

• Product Management
• Product Ownership
• Product Analysis

Including ownership of at least one AI or LLM-enabled product successfully deployed to production.

Pilot projects, demonstrations, prototypes, and proof-of-concepts do not satisfy this requirement.

2. AI Evaluation Ownership

Demonstrated ownership of acceptance criteria and evaluation processes for AI or LLM-enabled functionality.

Candidates should be able to clearly explain:

• Evaluation specifications
• Acceptance thresholds

that they personally defined.

3. RAG and LLM Evaluation Knowledge

Working knowledge of:

• Retrieval-Augmented Generation (RAG)
• Faithfulness
• Groundedness
• Retrieval Precision
• Retrieval Recall
• Answer Relevance
• Regression Testing across prompt and model versions

Enough depth to guide engineering teams, evaluate outputs, and challenge results without requiring direct coding responsibilities.

4. Business Case Development

Experience creating business cases that include:

• Defined baseline
• Success metric
• Measured outcome

Simply reporting hours saved is not sufficient.

5. Executive and Operational Stakeholder Management

Experience partnering with:

• Operational leaders
• Business stakeholders
• Executive leadership

Including:

• Intake management
• Opportunity scoring

6. Work Authorization

Must be:

• Authorized to work in the United States on a W-2 basis without sponsorship
• Physically located within the United States for the duration of the engagement

Preferred

The following strengthen a candidate profile but are not required.

Do not reject otherwise qualified candidates solely because these items are absent.

• Familiarity with Model Context Protocol (MCP), AI plug-ins, AI skills, AI asset catalogs, or internal capability marketplaces
• Experience with AI evaluation platforms such as:
  • RAGAS
  • DeepEval
  • Braintrust
  • LangSmith
  • Arize Phoenix
  • Weights & Biases Weave
  • OpenAI Evals
  • Similar evaluation frameworks
• Familiarity with AI governance frameworks, including:
  • NIST AI Risk Management Framework
  • NIST Generative AI Profile
  • ISO 42001
  • Colorado AI Act
  • Industry-specific AI governance requirements
• Experience operating in regulated environments where auditability, access controls, compliance, and logging are standard requirements
• Insurance, financial services, healthcare, or similarly regulated industry experience
• Familiarity with enterprise platforms such as:
  • Applied Epic
  • Vertafore
  • Microsoft Dynamics 365
  • Salesforce
  • Orion
  • Workday
  • Oracle Fusion
  • ACORD data standards
• Experience working within an AI or Automation Center of Excellence (CoE) or similar centralized operating model
• Experience supporting demand governance, portfolio management, and ongoing product rationalization efforts
• Exposure to RPA, UiPath, Microsoft Power Platform, or intelligent automation ecosystems
• Experience using Excel, Power BI, or similar tools for value analysis and portfolio reporting
• Jira or Azure DevOps experience for backlog management and delivery planning
• Bachelor's degree or equivalent practical experience

Certifications

No certification is required for this role, and demonstrated experience delivering successful AI-enabled products carries significantly more weight than certifications.

The following may be viewed as minor positives:

• Reforge
• Product School
• Pragmatic Institute
• Scrum.org Product Owner Certifications
• Microsoft AI Fundamentals (AI-900)

Certification Guidance

Do not reference, prioritize, or screen candidates based on:

These certifications are being retired and are not considered target credentials.

There is currently no broadly recognized individual certification for Anthropic Claude expertise, so candidates should not be evaluated based on possession of one.