Senior Data Scientist

Description

• 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

• 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

Interview process

Visa Sponsorship

Security clearance