Senior Product Manager - AI

What matters most

• Prioritize AI products by SQDC value

  Work with the AI Portfolio Manager to identify and prioritize several AI products, judging their impact through Safety, Quality, Delivery, and Cost.

• Shape and execute cross-functional roadmaps

  Provide roadmap input and coordinate business, engineering, research, data science, IT, and design work around strategic objectives.

• Deliver usable, technically rigorous AI

  Address user needs while working with technical teams to build robust solutions, verify success, and identify risks.

• Measure and improve products after launch

  Define and monitor metrics and KPIs, gather user feedback, and iterate on features to improve value delivery.

• Assess third-party AI products

  Oversee the introduction of AI products from software suppliers and determine their technology fit.

Likely challenges to think through

• Balancing competing measures of value

  Likely preparation hypothesis: An AI product could improve delivery or cost without improving safety or quality. Prepare a way to compare product options against all four SQDC dimensions and explain prioritization trade-offs; the posting does not specify how those trade-offs are resolved.

• Reconciling user needs with AI verification

  Likely preparation hypothesis: A feature users want may be difficult to verify or carry technical risks. Prepare an example of defining user value, agreeing on success checks with technical partners, and adjusting scope when evidence falls short.

• Coordinating decisions across businesses

  Likely preparation hypothesis: Different business groups and technical teams may favor different roadmap outcomes. Prepare to explain how you would surface those differences, make a recommendation tied to strategic objectives, and communicate it to varied stakeholders.

• Choosing when a supplier product fits

  Likely preparation hypothesis: A third-party AI product may promise faster delivery but fit user needs or technical requirements poorly. Prepare a comparison approach covering expected value, technical fit, verification, and risks without assuming GE Vernova uses a particular procurement process.

Topics to prepare

• SQDC value framework

  The posting says multiple AI products are prioritized and measured through Safety, Quality, Delivery, and Cost. Revise how an AI product might improve one dimension while creating a trade-off in another; explain what evidence would establish net value.

• Portfolio and roadmap alignment

  The role provides roadmap input alongside the AI Portfolio Manager and aligns products with strategic objectives. Revise the distinction between prioritizing several products and planning delivery within one product; practice explaining a prioritization decision when business needs compete.

• AI solution rigor and risk

  The posting calls for collaboration with data scientists, IT, and designers to verify success, identify risks, and develop robust AI solutions. Revise the questions you would ask before launch about intended use, evidence of performance, operational readiness, and who would address a failure. The questions are preparation suggestions, not stated employer criteria.

• User needs and post-launch value

  Understanding user needs, monitoring performance after launch, gathering feedback, and iterating are explicit responsibilities. Revise how to distinguish a feature being used from a feature improving the user's task; sketch what feedback would prompt a product change.

• Third-party AI technology fit

  The role assesses AI products from third-party software suppliers for technology fit. Revise the difference between an attractive demonstration and a solution that fits the intended users, technical environment, and value measures; use those dimensions to compare two hypothetical suppliers.

Skills to revise

• Draft a prioritized product roadmap

  Practice ranking three hypothetical AI opportunities, then write a short roadmap rationale showing strategic alignment, user need, expected SQDC impact, and an unresolved risk. This mirrors the stated work of shaping roadmaps across several products.

• Define and interpret product KPIs

  Create a sample KPI sheet for one hypothetical AI product: define a baseline, a measure of product performance, a measure of SQDC impact, and a review point. Practice deciding what to investigate if usage rises but measured value does not. The posting explicitly calls for analytics and KPI monitoring.

• Turn feedback into an iteration decision

  Given hypothetical post-launch feedback from users and a performance metric, write a one-paragraph decision to improve, retain, or pause a feature, including what you would check next. This rehearses the stated post-launch monitoring and iteration responsibility.

• Present a cross-functional decision

  Prepare a brief spoken update on an AI product choice for business, engineering, research, data science, IT, and design stakeholders. State the recommendation, evidence, risk, and decision needed; then condense it for an executive audience. Cross-functional work is a responsibility, while executive presentation is preferred.

• Compare supplier options

  Build a simple comparison of two hypothetical third-party AI products against user needs, technical fit, verification of success, and SQDC impact. Record what evidence is missing rather than declaring a winner on claims alone. This practices the posting's supplier-evaluation responsibility.

• Gather evidence for the stated requirements

  Prepare a concise timeline showing a bachelor's degree in a listed or related field and at least seven years of relevant professional experience. Identify examples of the preferred AI deployment and cross-functional leadership experience, if applicable.

• Build an AI product walkthrough

  Prepare one project story covering the user need, your product decisions, collaboration with business and technical teams, deployment, risks, and measured outcome. Distinguish your contribution from the team's.

• Practice a portfolio prioritization exercise

  Using hypothetical AI product opportunities, draft a one-page prioritization and roadmap recommendation. Explain trade-offs through the posting's Safety, Quality, Delivery, and Cost framework and how you would work with an AI Portfolio Manager.

• Sketch a technically rigorous product design

  For a hypothetical business-process use case, outline the user workflow, data and technical dependencies, success checks, and principal risks. Label the use case as an interview exercise, not a GE Vernova project.

• Prepare a measurement and iteration plan

  Define example KPIs tied to product performance and SQDC impact, then show how you would monitor results after launch, collect user feedback, and decide what to improve.

• Draft a third-party AI evaluation

  Create a short comparison template for supplier products covering user fit, technical fit, evidence of performance, risks, and trade-offs. The posting calls for evaluating suppliers but names no specific product.

• Confirm location and onboarding logistics

  Review the stated U.S. work-authorization and Niskayuna or Cambridge office requirements. If discussing Cambridge, be ready to address the required three-month hybrid onboarding residency in Niskayuna.

• Rehearse an executive summary and employer questions

  Practice a brief, outcome-focused explanation of your walkthrough for varied stakeholders. Ask how the team selects AI opportunities, verifies success and risk, coordinates across businesses, and measures post-launch SQDC impact.