Hybrid Diagnostics & Data Systems Engineer

Ford Motor Company

Dearborn (MI)

Hybrid

USD 110,000 - 150,000

Full time

8 days ago
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Job summary

Ford Motor Company in Dearborn, MI is seeking a Diagnostics & Data Systems Engineer for a hybrid role that combines remote work with on-site presence within commuting distance. The position targets advanced analysis of vehicle data streams and CAN logs to support robust diagnostic algorithms.

The engineer will develop Python-based scripts to query and categorize data from large cloud repositories (AWS and Google Cloud), build cloud-native tools, and enable rapid root-cause analysis across the

Qualifications

  • Experience engineering complex algorithms to analyze vehicle data streams.
  • Ability to define data acquisition requirements for onboard vehicle modules.
  • Proficiency in developing Python scripts for data querying and categorization from large-scale cloud data stores.

Responsibilities

  • Engineer and manage complex algorithms designed to automatically analyze vehicle data streams, critical for advanced identification, prediction, and root cause analysis of systemic issues within the vehicle's integrated electronic and software systems.
  • Analyze CAN communication logs to diagnose and isolate sources of faults and performance anomalies to improve diagnostic algorithms.
  • Execute queries on massive datasets from the vehicle fleet to identify patterns and correlations.

Skills

Python programming
CAN data analysis
Data analysis
Python scripting

Tools

AWS
Google Cloud

Job description

Ford Motor Company in Dearborn, MI is seeking a Diagnostics & Data Systems Engineer for a hybrid role that combines remote work with on-site presence within commuting distance. The position targets advanced analysis of vehicle data streams and CAN logs to support robust diagnostic algorithms.

The engineer will develop Python-based scripts to query and categorize data from large cloud repositories (AWS and Google Cloud), build cloud-native tools, and enable rapid root-cause analysis across the

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