Senior Data Engineer

This is a technical Individual Contributor (IC) role that requires a deep understanding of the full data science lifecycle. You won't just be building models in a vacuum; you will work cross-functionally with Product Managers, Industrial Designers, and Plant Engineers to ensure your solutions are robust, scalable, and integrated into the physical workflow of our factories. You will be a key player in Ford's transition to a software-defined manufacturing powerhouse.

Ford is undergoing its most significant transformation in over a century, and our manufacturing plants are at the heart of this evolution. In this role, you will sit within the ATP organization, focusing specifically on the "smart factory" initiative. You will be responsible for developing, deploying, and scaling machine learning models that address critical manufacturing challenges: predictive maintenance for robotics, real-time anomaly detection on the assembly line, and automated quality control.

* Model Development: Design and implement advanced machine learning models for predictive maintenance, anomaly detection, and computer vision-based quality control.

* End-to-End Pipeline Construction: Architect data pipelines from ingestion (sensor data, PLC logs) to model deployment and monitoring using GCP and Python.

* Statistical Analysis: Apply rigorous statistical methods to identify patterns in manufacturing data that correlate with vehicle quality or equipment downtime.

* Cross-Functional Collaboration:

 Partner with Product and Engineering teams to translate manufacturing pain points into technical requirements and deliver user-centric data products.

* Technical Leadership: Act as a subject matter expert within ATP, conducting code reviews, mentoring junior scientists, and staying at the forefront of AI/ML research in the industrial space.

* Scalability: Ensure models are optimized for production environments, moving from localized pilots to global plant-wide deployments.

* Data Strategy: Work with data engineering to improve data collection protocols and sensor telemetry quality from the plant floor.

* Model Development: Design and implement advanced machine learning models for predictive maintenance, anomaly detection, and computer vision-based quality control.

* End-to-End Pipeline Construction: Architect data pipelines from ingestion (sensor data, PLC logs) to model deployment and monitoring using GCP and Python.

* Statistical Analysis: Apply rigorous statistical methods to identify patterns in manufacturing data that correlate with vehicle quality or equipment downtime.

* Cross-Functional Collaboration:

 Partner with Product and Engineering teams to translate manufacturing pain points into technical requirements and deliver user-centric data products.

* Technical Leadership: Act as a subject matter expert within ATP, conducting code reviews, mentoring junior scientists, and staying at the forefront of AI/ML research in the industrial space.

* Scalability: Ensure models are optimized for production environments, moving from localized pilots to global plant-wide deployments.

* Data Strategy: Work with data engineering to improve data collection protocols and sensor telemetry quality from the plant floor.