Vehicle Connected Data Manager

Ford Ford · Auto · Dearborn, MI +1 · PD Operations and Quality

This role focuses on leveraging connected vehicle data and AI/ML techniques to predict and identify emerging quality issues before they impact customers. It involves developing advanced analytics frameworks, designing scalable algorithms, and leading technical investigations for root cause analysis across various vehicle systems. The position requires strong experience in automotive systems, big data analytics, and cross-functional leadership.

What you'd actually do

  1. Develop and deploy advanced analytics frameworks to identify anomalous vehicle behaviors, diagnostic trends, and emerging quality risks across millions of connected vehicles.
  2. Lead enterprise use of connected vehicle data throughout the product lifecycle, including pre-production validation, launch monitoring, and in-market quality surveillance.
  3. Lead use of connected data for quality across the entire product lifecycle, from TT builds in CVLQ to MP1 builds in EWRT and post-OKTB data in UCS.
  4. Drive development of automated analytics pipelines, AI-enabled monitoring tools, and executive dashboards supporting enterprise quality initiatives.
  5. Lead cross-functional initiatives focused on launch quality improvement, issue prevention, and reduction of customer-impacting failures.

Skills

Required

  • automotive systems expertise
  • big data analytics
  • AI/ML-driven detection methods
  • technical leadership
  • statistical methods
  • machine learning
  • scalable algorithms
  • data models
  • cloud-based telemetry
  • diagnostic data
  • software interaction data
  • detection logic
  • escalation frameworks
  • data-driven quality processes
  • software
  • hardware
  • calibration
  • systems integration
  • connected vehicle data
  • automated analytics pipelines
  • AI-enabled monitoring tools
  • executive dashboards
  • data engineering
  • platform teams
  • data accessibility
  • reliability
  • scalability
  • cloud environments
  • data science methodologies
  • operational analytics best practices
  • cross-functional initiatives
  • launch quality improvement
  • issue prevention
  • reduction of customer-impacting failures
  • executive communication
  • technical storytelling
  • Master’s degree in Data Science, Computer Science, Electrical Engineering, Statistics, Mathematics, or related technical field
  • 6+ years of experience in automotive engineering, connected vehicle analytics, data science, or quality analytics
  • 3+ years of experience leading cross-functional technical initiatives with measurable business outcomes
  • big data analytics platforms
  • cloud ecosystems including GCP, BigQuery, AWS, or Azure
  • scalable analytics solutions using large telemetry or sensor-based datasets
  • anomaly detection
  • predictive analytics
  • statistical modeling
  • machine learning applications
  • vehicle diagnostics
  • CAN/Ethernet communication architectures
  • DTC strategies
  • automotive functional domains including Powertrain, Electrified Powertrain, ADAS, Infotainment, Body Controls, Thermal Systems, and Vehicle Networking
  • automated data pipelines
  • operational analytics solutions supporting business-critical functions
  • Exceptional analytical, problem-solving, and cross-functional leadership capabilities

Nice to have

  • Programming experience in Python
  • embedded or systems-level scripting environments such as Lua
  • MBA or management-focused graduate education
  • Ford Connected Data practices – data engineering pipelines, data privacy standards, customer consent regulations
  • AI/ML model deployment and operationalization
  • connected vehicle telemetry architectures
  • cloud ingestion frameworks
  • visualization tools
  • executive analytics dashboards
  • structured root cause methodologies such as 8D or 5-Why
  • software-defined vehicle
  • over-the-air (OTA) ecosystems

What the JD emphasized

  • AI/ML-driven detection methods
  • AI-driven detection techniques
  • AI-enabled monitoring tools
  • Experience with AI/ML model deployment and operationalization

Other signals

  • AI/ML-driven detection methods
  • Apply statistical methods, machine learning, and AI-driven detection techniques
  • Design scalable algorithms and data models
  • Drive development of automated analytics pipelines, AI-enabled monitoring tools