Senior Data Scientist – Manufacturing Intelligence, Machine Learning & AI

Job summary

OVERVIEW At Ford, you’ll work on ideas that matter, alongside passionate people who want to make a global impact. Together, we’re shaping the next era of transportation—grounded in purpose, driven by progress. - Job Type: Full time - Work Type: Remote We are looking for a Senior Data Scientist to help build advanced analytics, machine learning, and AI solutions for manufacturing operations.

Benefits

Qualifications

• data quality, or model expectations are not realistic.
• 5+ years of experience applying data science, machine learning, statistical modeling, optimization, or advanced analytics in a professional environment.
• Strong Python
• data quality, and success criteria may need to be clarified.
• Professional confidence to challenge assumptions, push back constructively, and influence stakeholders with evidence.
• Demonstrated ability to learn new technical and business domains quickly.
• Preferred
• Experience applying data science or machine learning in manufacturing, industrial, automotive, aerospace, semiconductor, supply chain, quality, maintenance, or operations environments.
• Experience with automotive manufacturing, stamping, body shop, paint shop, final assembly, battery manufacturing, or powertrain operations.
• Understanding of manufacturing KPIs such as throughput, cycle time, downtime, OEE, JPH, FTT, FRC, scrap, rework, takt time, bottlenecks, quality escapes, and safety events.
• Basic understanding of manufacturing systems such as MES, SCADA, PLCs, historians, CMMS, QMS, ERP, or industrial IoT platforms.
• Hands-on experience with GCP services such as BigQuery, Cloud Storage, Pub/Sub, Dataflow, Vertex AI, Cloud Functions, Cloud Run, Looker, Cloud Build, Artifact Registry, or Cloud Monitoring.

Responsibilities

• Applied Machine Learning & Analytics
• Develop machine learning and statistical models to support manufacturing use cases such as anomaly detection, quality prediction, equipment health, process monitoring, throughput improvement, and decision support.
• Apply supervised, unsupervised, and semi-supervised learning methods, including classification, regression, clustering, anomaly detection, time-series analysis, statistical process control, and model explainability.
• Develop models for manufacturing use cases such as stamping split detection, weld quality, paint defects, assembly issues, predictive maintenance, bottleneck detection, process optimization, and quality prediction.
• Evaluate model performance using appropriate metrics, ground truth definitions, validation strategies, false positive and false negative analysis, and business impact measures.
• Identify when data is insufficient, labels are unreliable, ground truth is weak, or a machine learning approach is not appropriate, and communicate those limitations clearly.
• Manufacturing Data & Feature Engineering
• Analyze real-time and historical factory data from sources such as PLCs, sensors, machines, MES, SCADA, historians, quality systems, maintenance systems, production logs, and enterprise platforms.
• Create features from manufacturing signals such as cycle time, pressure, temperature, torque, vibration, current, force, cushion pressure, line speed, JPH, FTT, FRC, scrap, rework, downtime, and fault codes.
• Work with noisy, incomplete, high-frequency, or fragmented industrial data to create reliable analytical datasets.
• Build features that reflect manufacturing context, including asset hierarchy, station behavior, part flow, process sequence, shift patterns, tool usage, maintenance history, supplier variation, and quality outcomes.
• Partner with plant teams and domain experts to understand process behavior, validate assumptions, and determine whether model outputs reflect real operating conditions.

Skills

Degrees

Industry

Company size