Analytics Engineer

About Us

Are you ready to be part of a team that is bringing revolutionary technology to the remediation industries?

DISA Technologies is revolutionizing mineral recovery with our patented High-Pressure Slurry Ablation (HPSA) technology—an innovative solution that upgrades critical minerals from mined ore and legacy waste. Serving both the mining and remediation sectors, we recover valuable resources that power industry, strengthen energy independence, and restore contaminated sites to productive use. Our technology unlocks economic and environmental value, transforming how the world processes, remediates, and recycles essential mineral assets.

The Analytics Engineer designs, builds, and deploys advanced analytics and machine learning solutions to support DISA’s mineral recovery and remediation operations. This role applies AI/ML, numerical modeling, and statistical analysis to complex industrial data (e.g. time-series sensor data, particle size distributions, real-time process data) to improve equipment reliability, maintenance strategies, and process performance. The position works closely with engineering, operations, and maintenance teams to deliver practical, data-driven solutions that improve design decisions and operational efficiency in real-world environments.

The ideal candidate will have experience in applied mathematics and data analysis, with the ability to translate advanced concepts into working solutions in a fast-paced environment.

• Bachelor’s degree in Chemical Engineering, Mechanical Engineering, Industrial Engineering, Reliability Engineering, Data Analytics, Applied Mathematics, or a related technical discipline required.
• 3–7 years of experience in one or more of the following preferred:
  • Preventive or predictive maintenance modeling
  • Reliability or asset management engineering
  • Industrial analytics or operations data analysis
  • Applied machine learning in industrial or physical systems
• Demonstrated experience analyzing large operational data sets (sensor data, maintenance logs, inspections, downtime, and performance metrics).
• Experience in industrial environments such as mining, manufacturing, chemicals, energy, or minerals processing is strongly preferred.
• Candidates with backgrounds in reliability engineering, preventive maintenance, industrial operations, or field engineering who have applied AI or advanced analytics are strongly preferred.

Senior Process Engineer, unless otherwise designated.

• Analyze equipment, sensor, and maintenance data to identify degradation trends, failure modes, and reliability risks.
• Develop and deploy AI- and ML-enabled models for predictive maintenance, condition monitoring, and remaining useful life (RUL) estimation.
• Translate field and maintenance data into actionable insights that support design improvements and operational decisions.
• Partner with operations and maintenance teams to ensure analytical models align with real-world workflows and constraints.
• Apply advanced analytics to improve inspection strategies, spare parts planning, maintenance intervals, and asset performance.
• Support continuous improvement initiatives by quantifying the impact of design, process, or operational changes.
• Develop, review, and refine machine learning models supporting HPSA process optimization and predictive analytics.
• Apply numerical methods, statistical learning, and data-driven modeling to enhance system monitoring and control.
• Support integration of analytics and ML pipelines into production and operational systems.
• Document methodologies, maintain codebases, and ensure reproducibility and usability of results.
• Collaborate with all internal disciplines, and manage external partners, as necessary, on projects.
• Implement shape- and geometry-based statistical tools for manufacturing process monitoring.
• Support the integration of machine learning pipelines into production systems.
• Document methods, maintain codebases, and ensure reproducibility of results
• Abide by all policies and procedures established by DISA.
• Attend and participate in all required safety trainings.
• Assist with any task required by…