Senior Data Scientist

MetOx International, Inc.-posted about 5 hours ago
Full-time • Mid Level
Onsite • Houston, TX
51-100 employees
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Empower Your Future: At MetOx International, we’re pioneering the next era of energy security and abundance through breakthrough superconducting technology. As a Senior Data Scientist, you’ll join a dynamic team committed to strengthening the world’s energy systems to make them more resilient, efficient, and reliable. Based at our global headquarters in Houston, TX, the Senior Data Scientist will lead advanced analytics, modeling, and machine learning efforts across our manufacturing operations. In this role, you will partner closely with R&D, process engineering, quality, and operations to translate complex process challenges into data-driven insights that improve yield, quality, and scalability. This is a highly technical, hands-on role for someone who thrives at the intersection of process engineering, applied mathematics, and industrial data science, and who is excited to drive measurable impact in advanced materials manufacturing.

  • Develops and deploys statistical models for process characterization, optimization, and quality control in superconductor manufacturing.
  • Designs and executes design of experiments (DOE) to identify critical process parameters and optimize production outcomes.
  • Implements statistical process control (SPC) methodologies including multivariate control charts and process capability analysis.
  • Conducts root cause analysis using advanced statistical techniques combined with process engineering knowledge.
  • Builds physics-informed models that combine first-principles engineering with machine learning approaches.
  • Develops predictive models for yield optimization, defect detection, and predictive maintenance.
  • Applies time-series analysis and forecasting for process monitoring and anomaly detection.
  • Implements computer vision and machine learning solutions for automated quality inspection.
  • Applies advanced mathematical techniques including optimization theory, differential equations, and numerical methods to solve complex manufacturing challenges.
  • Develops digital twins and process simulation models for scenario analysis and process improvement.
  • Performs multivariate statistical analysis to understand complex interactions in manufacturing processes.
  • Builds decision support tools using mathematical optimization for production planning and resource allocation.
  • Designs and implements scalable data pipelines for real-time process monitoring across manufacturing operations.
  • Integrates data from multiple sources including SCADA systems, MES platforms, databases, and sensor networks.
  • Develops automated reporting systems for KPI tracking, process drift detection, and quality metrics.
  • Establishes best practices for data governance, version control, and reproducible research.
  • 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.
  • Translates complex process engineering challenges into tractable data science problems.
  • Communicates analytical findings and recommendations to technical and executive stakeholders.
  • Drives adoption of data-driven decision-making and advanced analytics across the organization.
  • Other duties as assigned
  • Bachelor's degree in Chemical Engineering, Materials Science, Applied Mathematics, Statistics, Data Science, or related technical field.
  • 4 years of professional experience in data science or analytics within manufacturing, process engineering, or industrial R&D environments.
  • Demonstrated experience in statistical process control, design of experiments, and process optimization.
  • Deep understanding of manufacturing processes, unit operations, and process dynamics.
  • Expertise in statistical process control (SPC), process capability analysis (Cp, Cpk), and control chart theory.
  • Proficiency with design of experiments including factorial designs, response surface methodology, and Taguchi methods.
  • Knowledge of quality management systems and continuous improvement methodologies.
  • Expert knowledge of multivariate statistics, time-series analysis, hypothesis testing, and Bayesian inference.
  • Strong foundation in linear algebra, calculus, differential equations, and optimization theory.
  • Experience with dimensionality reduction techniques (PCA, PLS, factor analysis).
  • Understanding of numerical methods and computational algorithms.
  • Advanced proficiency in machine learning techniques including regression, classification, ensemble methods, and neural networks.
  • Experience with computer vision and deep learning frameworks (TensorFlow, PyTorch, YOLO, etc.).
  • Knowledge of model validation, cross-validation, and hyperparameter optimization.
  • Familiarity with MLOps practices for model deployment and monitoring.
  • Expert-level programming in Python (NumPy, SciPy, Pandas, Scikit-learn, Matplotlib, Plotly).
  • Proficiency with statistical analysis software (Minitab, JMP, or equivalent).
  • Advanced SQL skills for complex data extraction and manipulation from manufacturing databases.
  • Experience with version control (Git), containerization (Docker), and CI/CD practices.
  • Familiarity with data visualization platforms (Grafana, Tableau, Power BI, Metabase).
  • Exceptional analytical and problem-solving abilities with attention to detail.
  • Strong communication skills with ability to explain complex mathematical and statistical concepts to diverse audiences.
  • Proven ability to lead projects and mentor team members.
  • Self-motivated with ability to work independently and manage multiple priorities.
  • Collaborative mindset with experience working across engineering, operations, and business functions.
  • Strategic thinking with ability to connect data insights to business objectives.
  • Master’s or PhD in Chemical Engineering, Materials Science, Applied Mathematics, Statistics, or related field.
  • Experience in process-intensive industries (semiconductor, chemical, pharmaceutical, or advanced materials manufacturing).
  • Six Sigma Black Belt or equivalent process improvement certification.
  • Experience with superconductor manufacturing, electrochemistry, thin-film deposition, or related materials processes.
  • Publication record in process engineering, applied statistics, or machine learning.
  • Experience with industrial automation systems (SCADA, MES, OPC, MQTT).
  • Strong foundation in mathematical modeling and advanced statistical methods
  • Health, dental, and vision available on the first day of employment
  • 401(k) match
  • Paid parental leave & adoption assistance
  • Educational reimbursement
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