ServiceNow Product Analyst – AI

By myKelly | August 25, 2026

ServiceNow Product Analyst – AI

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Job ID: mrp-884737

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IRVING, ,

Engineering

Contract,

$57.4 - $64.68

On-site

Other/Non Classified

IRVING, ,

$57.4 - $64.68

Contract,

Other/Non Classified

On-site

Engineering

Apply On Motion Recruitment Applied

Job details

Outstanding long-term contract opportunity! A well-known Financial Services Company is looking for a ServiceNow Product Analyst in Irving, TX or Charlotte, NC or Minneapolis, MN (Hybrid).

Work with the brightest minds at one of the largest financial institutions in the world. This is a long-term contract opportunity that includes a competitive benefit package! Our client has been around for over 150 years and is continuously innovating in today’s digital age. If you want to work for a company that is not only a household name, but also truly cares about satisfying customers’ financial needs and helping people succeed financially, apply today.

 

Contract Duration: 12 Months + extensions/conversion

 

Product Analyst role within our ServiceNow Core Platform AI Innovation team. 

 

Required Skills & Experience

  • AI acumen & knowledge using AI assisted tools like M365 Copilot, etc.
  • Ability to ask the right questions for helping to gather requirements.
  • Strong communication skills is a must.
  • Prefer someone who has some general knowledge of the ServiceNow platform.
  • Someone that understands the SN capabilities
  • Must be able to engage with all levels, (directors, VP etc.)
  • Understanding the AI – language prompts

 

 

What You Will Be Doing

  • Ensure traceability and lifecycle management of AI use cases and aligning all AI solutions to required risk models for the ServiceNow platform.
  • Assist in coordinating inputs across tenants and development teams around AI initiatives and ensuring production readiness standards are met for AI deployments.
  • Beyond governance, this role is embedded within the AI Innovation & Incubation delivery team, supporting sprint execution, backlog readiness, testing and validation coordination, and cross-team dependency management across engineering, architecture, and governance stakeholders. It serves as a critical connector between product, engineering, and governance functions; ensuring that AI use cases are not only delivered efficiently but are also aligned to enterprise standards from inception through deployment.

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