Builds decision support tools using mathematical optimization for production planning and resource allocation.
Develops automated reporting systems for KPI tracking, process drift detection, and quality metrics.
Leads cross-functional data science projects involving R&D, process engineering, quality, and operations teams.
Mentors junior data scientists and engineers on statistical methods, machine learning, and best practices.
Implements computer vision and machine learning solutions for automated quality inspection.
Develops and deploys statistical models for process characterization, optimization, and quality control in superconductor manufacturing.
Implements statistical process control (SPC) methodologies including multivariate control charts and process capability analysis.
Designs and implements scalable data pipelines for real-time process monitoring across manufacturing operations.
Performs multivariate statistical analysis to understand complex interactions in manufacturing processes.
Develops predictive models for yield optimization, defect detection, and predictive maintenance.
Builds physics-informed models that combine first-principles engineering with machine learning approaches.
Communicates analytical findings and recommendations to technical and executive stakeholders.
Develops digital twins and process simulation models for scenario analysis and process improvement.
Designs and executes design of experiments (DOE) to identify critical process parameters and optimize production outcomes.
Translates complex process engineering challenges into tractable data science problems.
Applies time-series analysis and forecasting for process monitoring and anomaly detection.
Applies advanced mathematical techniques including optimization theory, differential equations, and numerical methods to solve complex manufacturing challenges.
Conducts root cause analysis using advanced statistical techniques combined with process engineering knowledge.
Integrates data from multiple sources including SCADA systems, MES platforms, databases, and sensor networks.
Drives adoption of data-driven decision-making and advanced analytics across the organization.
Establishes best practices for data governance, version control, and reproducible research.
Requirements
python
mlops
spc
six sigma
phd
machine learning
Bachelor's degree in Chemical Engineering, Materials Science, Applied Mathematics, Statistics, Data Science, or related technical field.
Demonstrated experience in statistical process control, design of experiments, and process optimization.
Experience with superconductor manufacturing, electrochemistry, thin-film deposition, or related materials processes.
Strategic thinking with ability to connect data insights to business objectives.
Experience with industrial automation systems (SCADA, MES, OPC, MQTT).
Knowledge of quality management systems and continuous improvement methodologies.
Strong foundation in linear algebra, calculus, differential equations, and optimization theory.
Advanced proficiency in machine learning techniques including regression, classification, ensemble methods, and neural networks.
Machine Learning & AI
Knowledge of model validation, cross-validation, and hyperparameter optimization.
4 years of professional experience in data science or analytics within manufacturing, process engineering, or industrial R&D environments.
Self-motivated with ability to work independently and manage multiple priorities.
Advanced SQL skills for complex data extraction and manipulation from manufacturing databases.
Publication record in process engineering, applied statistics, or machine learning.
Soft Skills & Leadership
Master’s or PhD in Chemical Engineering, Materials Science, Applied Mathematics, Statistics, or related field.
Expert knowledge of multivariate statistics, time-series analysis, hypothesis testing, and Bayesian inference.
Proficiency with statistical analysis software (Minitab, JMP, or equivalent).
Familiarity with data visualization platforms (Grafana, Tableau, Power BI, Metabase).
Experience with computer vision and deep learning frameworks (TensorFlow, PyTorch, YOLO, etc.).
Exceptional analytical and problem-solving abilities with attention to detail.
Expertise in statistical process control (SPC), process capability analysis (Cp, Cpk), and control chart theory.
Experience with version control (Git), containerization (Docker), and CI/CD practices.
Familiarity with MLOps practices for model deployment and monitoring.
Collaborative mindset with experience working across engineering, operations, and business functions.
Strong communication skills with ability to explain complex mathematical and statistical concepts to diverse audiences.
Proven ability to lead projects and mentor team members.
Experience with dimensionality reduction techniques (PCA, PLS, factor analysis).
Experience in process-intensive industries (semiconductor, chemical, pharmaceutical, or advanced materials manufacturing).
Advanced Modeling & Machine Learning
Six Sigma Black Belt or equivalent process improvement certification.
Process Engineering & Domain Knowledge
Strong foundation in mathematical modeling and advanced statistical methods
Understanding of numerical methods and computational algorithms.
Deep understanding of manufacturing processes, unit operations, and process dynamics.
Expert-level programming in Python (NumPy, SciPy, Pandas, Scikit-learn, Matplotlib, Plotly).
Proficiency with design of experiments including factorial designs, response surface methodology, and Taguchi methods.
Benefits
Health, dental, and vision available on the first day of employment
Educational reimbursement
401(k) match
Paid parental leave & adoption assistance
Training + Development
Information not given or found
Interview process
Information not given or found
Visa Sponsorship
Information not given or found
Security clearance
Information not given or found
Company
Overview
Provides high-tech solutions that drive progress and innovation in various sectors.
Operates across diverse industries, including energy and infrastructure.
Integrates advanced technology into practical applications, emphasizing efficiency and sustainability.
Customizes solutions to meet the unique needs of its clients.
Pioneers in digital transformation, modernizing traditional industries with cutting-edge technologies.
Collaborates with both public and private sector clients to deliver impactful, large-scale projects.
Recognized for managing complex, multi-disciplinary projects with a focus on quality and cost-effectiveness.
Culture + Values
Growth Mindset
Openness
Team‑Built Success
Environment + Sustainability
120M Secured
Grant and Funding
Secured $80 million U.S. Department of Energy grant and $40 million Series B funding to expand domestic production capacity and support clean energy infrastructure.
193.7M Facility
Manufacturing Investment
Planning a $193.7 million manufacturing facility in Chatham County, NC, projected to create 333 jobs and reduce grid carbon emissions via HTS wire deployment.
Scaling Xeus™ HTS wire manufacturing to enable high‑efficiency grid expansion and renewable energy integration with near-zero energy loss in transmission.
Xeus™ wire technology designed to support transmission from zero-carbon generation (wind, solar, fusion) and reduce losses compared to copper conductors.
'Wire the World for Net Zero' indicating corporate commitment to net‑zero energy infrastructure.
Inclusion & Diversity
Inclusive policies are stated as core to the workplace.
Membership support is provided for Women in Manufacturing.
Membership support is provided for Heroes Make America.