Process and Analytics Engineer

Koch Business Solutions, LP; Wichita (KS); On-site; USD 85,000 - 110,000; Full time

Job summary

A leading energy solutions company is seeking a Process and Analytics Engineer in Wichita, Kansas. The successful candidate will leverage analytical solutions to improve equipment performance and collaborate across disciplines. Responsibilities include developing monitoring tools, supporting anomaly detection, and applying complex analytical methods. Basic qualifications include process industry experience and proficiency in industrial data systems. Preferred qualifications include a Bachelor's in Chemical Engineering and hands-on analytics experience.

Qualifications

• 4+ years in process engineering or monitoring roles.
• Experience with anomaly detection and analytical methods.
• Hands-on experience developing production analytics.

Responsibilities

• Partner with internal customers to apply engineering solutions.
• Support anomaly detection through empirical and engineering models.
• Develop monitoring tools using analytical methods and software.

Skills

Process or reliability industry experience

Ability to read and interpret process data

Knowledge of industrial data systems

Proficiency in R and Python

Education

Bachelor's degree in Chemical Engineering

Tools

PI Asset Framework

HYSYS

Machine Learning tools

Job description

Overview

Process and Analytics Engineer

Koch Ag and Energy Solutions is looking for a Process and Analytics Engineer to join our KAES Monitoring and Diagnostics Capability to advance application of technology in the process and equipment health space. The KAES Monitoring & Diagnostics Capability is a cross‑site partnership with KAES's operating facilities, providing collaboration and support across multiple disciplines of expertise to automatically detect process and equipment performance deviations in real time.

This role is located at our main campus in Wichita, Kansas. This role is not eligible for Visa Sponsorship.

What You Will Do

• Partnering with internal customers to understand business problems and apply engineering and analytical solutions and developing collaborative workflows that enable better/faster decisions
• Supporting anomaly detection monitoring efforts through creation and improvement of complex empirical and engineering models in partnership with technical SMEs and monitoring team
• Use tools like PI Asset Framework and PI Vision to develop monitoring tools
• Apply more complex analytical methods such as HYSYS, Advanced Pattern Recognition, Machine Learning and scripting (R, python) as needed to meet functional needs
• Support and train base organization and SMEs with analytical tools such as Pi Asset Framework, Seeq, HYSYS online
• Coordinate with 3rd party contractors through larger build‑out and execution efforts
• Partnering with IT capabilities to refine analytical and data systems to meet expanding needs
• Screening detected process and equipment performance anomalies and communicating issues to sites

Who You Are (Basic Qualifications)

• Process or reliability industry experience working with refinery, chemical, or related process equipment technologies
• Demonstrated ability to read and interpret process and equipment performance data (examples: root‑cause investigations, basic statistical trending)
• Demonstrated working knowledge of industrial data systems (e.g., OSIsoft PI/PI AF, data historians)
• Willing and able to travel up to 10%

What Will Put You Ahead

• 4+ years of process industry experience in operations, reliability, process engineering, or monitoring/diagnostics roles
• Bachelor's degree in Chemical Engineering (preferred)
• Hands‑on experience developing production analytics or data solutions
• Experience working within Agile delivery teams and executing experiments or iterative pilots

Equal Opportunity Employer, including disability and protected veteran status. Except where prohibited by state law, some offers of employment are conditioned upon successfully passing a drug test. This employer uses E-Verify. Please click here for additional information.