Manager, Uptime AI

Ford Ford · Auto · Dearborn, MI +1 · Global Data Insight & Analytics

Product Manager for Uptime AI at Ford, focused on transforming the Customer Service Division's ecosystem into a proactive, AI-driven pipeline. The role centers on the Quality Early Warning (QEW) system, utilizing LLMs and advanced analytics to detect vehicle concerns early, reduce repair order duration, and prevent SLA breaches. Responsibilities include collaborating with data scientists, leading proactive system innovation, developing strategic action plans, and owning dashboards and executive storytelling.

What you'd actually do

  1. AI & Data Science Collaboration: Partner with GDIA Data Scientists to extract and analyze multi-source data for early trend detection and root-cause identification. You will continuously refine predictive dashboard models, partner with GDIA Data Scientists to ingest, clean, and synthesize complex, multi-source data streams (including connected vehicle telemetry, warranty claims, and customer feedback), accelerating early trend detection of emerging quality issues, and ensure data integrity by validating AI outputs from a business, quality, and engineering perspective.
  2. Proactive System Leadership: Drive the innovation of quality processes by scaling "Field Intelligence" and "Command Center" models. Integrate these frameworks with Uptime logic to transform how the organization identifies unknown issues - moving from reactive reporting to real-time, predictive intervention that reduces repair order duration.
  3. Lead Strategic Action Plans: Develop robust business cases and prioritized action plans for top emerging issues (e.g., software deployment failures, hardware defects) to minimize financial impact and warranty exposure.
  4. The FCSD Product Uptime Manager Liaison acts as the ultimate bridge between advanced data science (GDIA), core Product Engineering, Quality, and Service Engineering Operations (SEO). In this role, you will represent FCSD interests across the product lifecycle, leveraging predictive AI and regional Rapid Hubs to identify emerging issues, validate root causes, and rapidly deploy Permanent Corrective Actions (PCAs) to protect the customer experience and reduce warranty exposure.
  5. Proactive Uptime Triage: Utilize statistical analysis and AI-driven "Service Complexity Predictors" to identify at-risk repairs. Specifically target and prevent long repair order duration.

Skills

Required

  • Bachelor's degree in engineering, statistics, or related field of technology
  • Proven ability to utilize data mining and visualization tools to identify patterns and anomalies in unstructured data
  • Confident in leveraging, implementing, and scaling artificial intelligence solutions (such as LLMs and predictive models) to move from pilot concepts to enterprise-wide operational tools
  • Capability of organizing and managing numerous high-stakes projects simultaneously from inception/feasibility through global implementation
  • Solid written and verbal skills to communicate technical results and project status to cross-functional stakeholders
  • Overall knowledge of vehicle systems and operations
  • Understanding of diagnostics and repair procedures required
  • Strong interpersonal skills; ability to work with other areas of the company
  • Able to work independently with minimal supervision
  • Strong project management skills with the capability of organizing & managing numerous projects simultaneously from inception/feasibility, through implementation

Nice to have

  • Experience with Large Language Models (LLMs), clustering, Artificial intelligence or embeddings used to analyze customer data
  • Proficiency in Six Sigma, 8D, 5-Why, or other structured root-cause methodologies
  • A self-starter who is comfortable being self-directed in a high-volume, fast-paced environment

What the JD emphasized

  • primary owner of the Quality Early Warning (QEW) system
  • leveraging Large Language Models (LLMs) and advanced analytics
  • AI & Data Science Collaboration
  • scaling "Field Intelligence" and "Command Center" models
  • AI-driven "Service Complexity Predictors"

Other signals

  • Leveraging Large Language Models (LLMs) and advanced analytics to detect emerging vehicle concerns
  • Transforming Ford’s Customer Service Division ecosystem from reactive problem-solving to a proactive, AI-driven pipeline
  • Eliminate manual guesswork in quality, empowering teams with precise, automated insights