Prognostics and Diagnostics Engineer at Xerox PARC

PARC is incubating a new software venture in the Industrial Internet of Things (IIoT) space, based on industry-leading technology developed at PARC. Building on a world-class group of scientists and engineers with decades of experience in system health monitoring, the new venture has ambitions to redefine how industrial equipment is managed.

We are looking for a Predictive Maintenance (PdM) Engineer who will be responsible for developing methods and models for anomaly detection, diagnostics, and prognostics of chemical process or plant equipment failure using streaming sensor data from industrial equipment as input. Reporting to the Chief Science Officer, the PdM Engineer will be responsible for developing mathematical models of chemical processes for real-time parameter estimation and process optimization.

The PdM Engineer will also be responsible for developing physics-based models of assets commonly used in a large number of different industrial environments, primarily in the chemical industry, for the purposes of anomaly detection, diagnostics, or prognostics of asset or process failure. In this role, the PdM Engineer is expected to interact with clients in the chemical (pharma/O&G/ process…) industry to understand their operational issues, to formulate potential solutions to these issues, to implement novel tools to address client concerns, and to oversee deployment and commissioning of these solutions at process plants. He/she will bring the ability to work in a multi-disciplinary team of Mechanical, and Chemical engineers, Computer Scientists and algorithm designers, and experience in developing algorithms for production environments.

OTHER RESPONSIBILITIES:
Collect, clean and analyze historical and new data from industrial equipment
Identify needs for additional data collection and needs for sensor deployment, including selection and placement of suitable sensors to meet the requirements of the models and algorithms
Build end-to-end production algorithms for asset anomaly detection, diagnostics, and prognostics, and for process optimization
Build physics-based models that represent equipment behavior a) under nominal conditions; b) when subject to different degradation modes
Build reproducible tests for proposed models / algorithms
Build end to end algorithms for anomaly detection, diagnostics, prognostics, and process optimization
Come up with creative ways to find and use algorithms that significantly impact performance metrics
Work in close collaboration with software engineering to deliver production-grade code ready for industrial implementation

Requirements:
Advanced degree (Ph.D. preferred) in Engineering (Chemical, Mechanical, Electrical, …), Applied Physics, Computer Engineering or similar from a recognized university
5+ years of experience working as a Process Engineer designing, developing, and troubleshooting chemical processes;
Experience as contributor on a range of projects – track record of delivering mature component degradation models and algorithms
Excellent communication skills – Must be able to articulate analysis and methodologies clearly and communicate insights in an accessible way to the team
Must be able to conceive of solutions that have not been already defined
Ability to write code in Python
Experience working with large real-time data streams

#LI-DP1

The primary job location is Palo Alto, CA. Please note that the position will require occasional travel (up to 20%), mostly within North America. Remote work (including East Coast or Midwest locations) is negotiable for experienced candidates with demonstrated skills in managing complex projects without direct supervision.

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