Forward Model & Issue Resolution Quality Insights Supervisor

Ford Motor Company•Dearborn, MI

3d•$150,000 - $180,000•Hybrid

About The Position

The Forward Model & Issue Resolution Quality Insights Supervisor supports Forward Model Quality and works closely with system triage and issue resolution teams. The role helps upcoming vehicle programs launch with fewer electronics and software issues by driving issues to resolution, turning issue data into actionable insights, and helping teams organize and focus their AI efforts. The supervisor identifies opportunities for AI to improve daily work, connects overlapping efforts, shares effective practices, and helps turn promising ideas into everyday tools. The role owns definitions, data quality, dashboards, and reporting cadence that provide leaders with a clear, trusted view of system triage, issue resolution, and forward model quality. Working across quality, engineering, and triage leadership, the supervisor aims to deliver measurable gains in quality, efficiency, issue-resolution performance, and launch readiness. The position is hybrid, with a requirement to be onsite four or more days per week.

Requirements

Bachelor’s degree in Software Engineering, Electrical Engineering, Computer Science, Data Science, Systems Engineering, Mechanical Engineering, or a related field.
At least 5 years of experience in automotive systems, software, electronics, quality, systems engineering, product development, or a related technical discipline, including experience driving cross-functional projects or issues to completion.
Demonstrated experience managing technical programs or projects, including scope, schedules, dependencies, risks, and status reporting.
Experience defining metrics, building dashboards, and using data to track performance, identify trends, prioritize work, and improve issue-resolution outcomes.
Experience supporting, deploying, or using AI-enabled or digital solutions to improve technical or business outcomes, and ability to translate operational problems into practical use cases.
Working knowledge of vehicle electronics and software, including architecture, interfaces, diagnostics, failure modes, and system-level troubleshooting.
Strong communication and collaboration skills, including the ability to drive accountability across cross-functional teams and clearly present status, metrics, and risks to leaders.
Ability to lead or coordinate a small team, develop others, and deliver results through disciplined execution and influence.
Legally authorized to work in the United States; employment eligibility will be verified at hire.
Visa sponsorship is not available for this position.

Nice To Haves

Master’s degree in a technical, data, artificial intelligence, or management field, or a project management certification such as PMP.
At least 7 years of experience in automotive electrical, electronic, software, systems, or quality engineering.
Experience helping engineering teams identify, organize, and deliver AI, analytics, or automation ideas from concept through everyday use.
Experience supervising a small technical team or leading work across a matrixed organization.
Experience in forward model or new-vehicle quality, including prototype builds, launch readiness, and rapid response to electrical and software quality concerns.
Proficiency with dashboarding and data tools such as Power BI, Jira dashboards, SQL, or Python.
Working familiarity with generative AI, machine learning, intelligent automation, evaluation methods, and responsible-AI controls, including human review before AI results influence engineering or quality decisions.
Knowledge of Ford product-development and issue-management processes and tools, including AVBOM, WERS, AIMS, 8Ds, eFDVS, Jama, Engineering Sign Off, DFMEA, Six Sigma, Confluence, and Jira applications.
Experience planning or running hackathons, pilots, or rapid experiments.
Curiosity and a strong bias for practical application, experimentation, learning, and measurable improvement in engineering quality and efficiency.

Responsibilities

Support rapid cross-functional response to emerging electronics and software issues during forward model development, prototype and pre-production builds, launch, and early production; track issues through containment and resolution, support launch-readiness reviews, and capture lessons learned.
Drive electronics and software issues on forward model programs to verified closure ahead of key program milestones.
Define and track forward model quality measures, including issue burn-down to milestones, repeat issues, and 8D closure timing, and report launch readiness to leadership.
Use current- and past-model insights to inform design reviews, DFMEAs, test plans, and lessons learned to prevent repeat issues in future programs.
Help system triage and issue resolution teams organize AI work by maintaining a shared view of ideas and projects, supporting prioritization, connecting overlapping efforts, and focusing work on real operational problems.
Help teams identify existing tools, data, and approved platforms, and connect them with AI, data, and cybersecurity contacts so their work remains safe and supportable.
Supervise a small team that coaches engineers on practical AI use through hands-on sessions, office hours, and shared examples; capture and share effective practices for reuse.
Drive electronics and software issues to closure by confirming ownership, setting next steps and due dates, removing blockers, escalating at-risk issues, and holding teams accountable for containment, root cause, corrective action, and verified closure.
Support system-level triage and troubleshooting using vehicle, system, and cross-feature knowledge; use AI tools to improve signal detection, evidence synthesis, issue routing, and prioritization.
Own system triage and issue-resolution metrics, including definitions, data sources, targets, dashboards, and reporting cadence; track measures such as time to triage and resolution, issue aging, backlog, throughput, reopen rate, issue-intake quality, and AI adoption, and use them to identify trends and drive action.
Analyze issue, warranty, and field data to identify recurring failure modes and systemic causes, and translate insights into practical actions for engineering and triage teams.
Establish and maintain data standards, classification fields, and Jira hygiene to make metrics accurate, consistent, and trusted across test, triage, systems, and development teams.
Execute and continuously improve issue intake, triage, escalation, reprioritization, closure, and learning processes; reinforce ownership, criteria, and decision paths, and integrate AI with appropriate traceability and human review.
Support hackathons and small pilots focused on triage and issue-resolution problems; help evaluate results, assign owners, and transition successful ideas into everyday tools.
Prepare executive-ready reporting on metrics, insights, and risks, and partner with product, program, software, hardware, systems engineering, validation, manufacturing, service, and quality teams to align stakeholders.