Service Delivery & Contract Management AI Product Owner

When joining PerkinElmer, you select an experienced and trusted leader in scientific solutions, with the support of a global service network and distribution centers, providing the right solution, at the right time, to meet critical customer needs. With over an 80+ year legacy of advancing science and a mission of innovating for a healthier world, our dedicated team collaborates closely with commercial, government, academic and healthcare customers to deliver our broad portfolio of analytical solutions, and OneSource services.

Job Title

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Job Description

Role Purpose

The Service Delivery & Contract Management AI Product Owner is a combined role that owns two connected domains of AI-enabled operational improvement: the service delivery framework and the contract management decision logic.

Across both domains, this role transforms operational data into predictable, explainable, and auditable guidance — driving productivity improvements in work order management and ensuring service contract decisions are consistent, evidence-based, and optimized across the asset fleet.

This role designs and governs AI-powered assistants that support engineers, planners, operational teams, and commercial stakeholders by:

• Improving work order quality and reducing approval delays
• Enabling proactive maintenance and root cause identification
• Ensuring the right vendor is engaged quickly
• Delivering consistent, defensible contract recommendations
• Supporting renewal decisions and retrospective contract performance reviews

This role does not build AI models or write code. It defines what questions the AI can answer, what data it uses, what rules it follows, and how recommendations are explained.

The role holder must bring a marginal gains mindset — continuously improving processes for ongoing productivity gains.

Key Responsibilities

1. Work Order Productivity & Process Improvement

Drive measurable productivity improvements in the work order management process.

• Map the work order lifecycle (creation → triage → execution → closure)
• Identify non-value-adding steps, rework loops, approval delays, and manual handoffs that AI-assisted guidance could help eliminate or reduce.
• Apply lean principles to reduce:

• Number of work order steps
• Work order handling time
• Administrative effort for engineers and planners

• Define AI-supported guidance and automation opportunities that simplify and standardize work order execution

2. Proactive Service Management & Alerts

Lead the shift from reactive repairs to proactive service intervention.

• Define how service alerts are generated based on failure history, downtime trends, utilization intensity, and asset age
• Establish clear thresholds for early intervention
• Ensure proactive insights are explainable, actionable, and trusted by frontline teams

3. Root Cause Analysis & Repeat Repair Identification

Strengthen identification of the true underlying causes of equipment failures.

• Define how root cause evidence is captured and interpreted in ServiceMax
• Use structured data and work order notes to distinguish repeat faults, identify systemic failure patterns, and reduce symptom-based fixes
• Ensure root cause insights inform service strategy and preventive actions

4. AI Assistant & Virtual Work Order Specialist

Define and govern an AI assistant that acts as a virtual work order specialist.

• Specify functional requirements for an agent that supports correct work order creation, prompts for missing information, and surfaces relevant historical service insights
• Define where the agent may initiate the front end of service requests or communications via ServiceMax integrations
• Ensure outputs clearly explain: what is happening, why it matters, what action is recommended, and what alternatives exist

5. Vendor Performance & Capability Insights

Provide visibility into vendor performance beyond individual assets.

• Define how vendor trends are analyzed across asset classes, sites or regions, and response and resolution times
• Support identification of appropriate vendors when assets are not on contract
• Enable comparisons based on capability, speed of response, and repeat visit rates

6. Define What Good Looks Like for Contract Recommendations

Specify mandatory elements for every AI contract recommendation, including:

• Contract status and entitlement
• Warranty position
• Asset criticality and utilization
• Failure and downtime history
• Strategic supplier linkage (e.g. wider supplier grouping)
• Also define what the AI must never infer, guess, or assume

7. Contract Data Ownership & Quality

Own the contract data model linking assets, contracts, entitlements, and work orders.

• Identify and take accountability to rectify data quality gaps in OneSource / ServiceMax
• Maintain the system linkage between contract records and operational performance data

8. Contract Decision Rules

Translate operational practice into decision logic, including:

• Non-negotiable rules (warranty, regulatory)
• Best-practice guidance (risk-based coverage)
• Prioritization logic (criticality vs redundancy vs cost vs uptime)
• Retrospective review logic — have the right decisions historically been made on this asset? Ad hoc spend vs contract spend

9. Service & Contract AI Tool Design & Wireframing

Define functional requirements and wireframes across both domains for:

• Work order guidance and automation
• Failure risk indicators and PM effectiveness assessments
• Contract suitability assessments
• Renewal recommendations and stakeholder engagement workflows

10. Validation, Adoption & Continuous Improvement

• Lead user validation with engineers, operations leads, FM, sourcing, and end users
• Review overridden or rejected recommendations in both service and contract domains
• Update rules as contracts, suppliers, service strategies, or policies change
• Monitor recommendation accuracy, relevance, and data drift over time

Service Data Mapping & Integrity

Own the service data model linking:

Assets → Work Orders → Failure Modes → Outcomes

• Identify data gaps, inconsistencies, and misclassification
• Define data standards required to support service insights, productivity analysis, and AI-supported recommendations

Critical Skills

• Must have strong understanding of operations, service contracts, warranties, and asset risk
• Must have high data literacy — able to work with data and interpret analytical outputs without coding experience
• Must have proven analytical experience
• Must have demonstrated ability to convert tacit operational knowledge into explicit, auditable rules
• Must have strong stakeholder facilitation and governance discipline
• Must have a marginal gains mindset — committed to continuous process improvement and ongoing productivity gains

Basic Qualifications:

• Bachelors Degree and 7+ years of relevant work experience OR
• Associates Degree and 9+ years of relevant work experience OR
• Highschool Diploma and 12+ years of relevant work experience

Working Environment:

• Job pace may be fast and job completion demands may be high.
• Must be able to remain in a stationary position more than 25% of the time
• Occasionally move or lift up to 25 pounds (potential for occasional lifting of up to 50 pounds).
• Occasionally operates a computer and other office machinery, such as a calculator, copy machine, and computer printer.