Forward Model & Issue Resolution Quality Insights Supervisor

Brings together Ford’s vehicle, electric, digital and design teams with our global manufacturing system. Working as one end-to-end organization, we turn ideas into high-quality vehicles, software and experiences across gas, hybrid and electric, designed around what customers need.

This role supports Forward Model Quality and works in close partnership with system triage and issue resolution teams.

You will help ensure that upcoming vehicle programs launch with fewer electronics and software issues by driving issues to resolution, turning issue data into actionable insights, and helping system triage, issue resolution, and quality teams organize and focus their AI efforts. You will help teams find where AI can make their work easier, connect overlapping efforts, share what works, and turn promising ideas into tools that teams use every day.

You will also own the definitions, data quality, dashboards, and reporting cadence that give leaders a clear, trusted view of triage, resolution, and forward model quality health. Working across quality, engineering, and triage leadership, you will deliver measurable gains in quality, efficiency, issue-resolution performance, and launch readiness.

• Launch issue response:
  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 for engineering.

• Milestone issue closure:
  Drive electronics and software issues on forward model programs to verified closure ahead of key program milestones, so that programs reach launch with a clean, well-understood issue list.

• Launch quality metrics:
  Define and track forward model quality measures such as issue burn-down to program milestones, repeat issues, and 8D closure timing, and report launch readiness to leadership.

• Repeat issue prevention:
  Use insights from current and past models to inform forward model design reviews, DFMEAs, test plans, and lessons learned so the same issues do not carry into future programs.

• Organizing team AI efforts:
  Help system triage and issue resolution teams organize their AI work by maintaining a shared view of active ideas and projects, helping teams set priorities, connecting overlapping efforts, and keeping work focused on real triage and resolution problems.

• AI guidance and connections:
  Help teams find existing tools, data, and approved platforms before building something new, and connect them with the right AI, data, and cybersecurity contacts when needed so their work stays safe and supportable.

• AI enablement and sharing:
  Supervise a small team that coaches engineers on practical AI use through hands‑on sessions, office hours, and shared examples. Capture and share what works so teams can reuse solutions instead of starting over.

• Issue resolution driving:
  Drive electronics and software issues to closure by confirming ownership, setting clear next steps and due dates, removing blockers, and escalating at-risk issues. Hold teams accountable for timely containment, root cause, corrective action, and verified closure.

• Triage support:
  Apply vehicle, system, and cross‑feature knowledge to support system‑level triage and troubleshooting. Use AI tools to improve signal detection, evidence synthesis, issue routing, and prioritization.

• Metric leadership:
  Own the metrics for system triage and issue resolution, including definitions, data sources, targets, dashboards, and reporting cadence. Track measures such as time to triage, time to resolution, issue aging, backlog, throughput,…