Sr. Staff Data Scientist

Key Responsibilities

• Design and develop Python-based tools, pipelines, and automated workflows for engineering analysis
• Build, deploy, and maintain digital twins, soft sensors, and advanced analytics models for process optimization
• Analyze large-scale manufacturing datasets (including time-series and historian data) to identify opportunities in yield, throughput, and reliability
• Partner cross-functionally with process engineers, data scientists, and software engineers to operationalize solutions in production environments
• Translate complex process engineering challenges into scalable data models and software implementations
• Own ambiguous, high-impact problems and drive them from model concept through validation, deployment, and monitoring

Required Qualifications

• MS or PhD in Chemical Engineering, Mechanical Engineering, Electrical Engineering, or a related field in the physical sciences (e.g., Physics, Chemistry, Applied Mathematics)
• 6+ years of industry experience, with demonstrated impact at a senior or staff level in industrial, manufacturing, or process-oriented environments
• Strong ability to translate physical systems and engineering/scientific problems into data-driven models and production-grade code
• Solid foundation in first principles, physical systems, and process understanding, with the ability to connect theory to real-world applications
• Experience working with complex systems involving sensors, instrumentation, or process data

Core Skills

• Process modeling & engineering fundamentals
• First-principles modeling, scale-up, and root-cause analysis
• Programming & data analysis (Python, NumPy, Pandas, SciPy, visualization libraries)
• Automation of engineering calculations and analytical workflows
• Data engineering & analytics (large-scale datasets, time-series analysis, and process historian data)
• Machine learning for physical systems (statistical modeling, hybrid modeling, or ML applied to process optimization)
• Problem-solving & ownership (ability to operate in ambiguous environments and deliver end-to-end solutions)

Nice-to-Have (Optional but Valuable)

• Experience with digital twin platforms or industrial AI frameworks
• Familiarity with cloud environments (Azure, AWS, or GCP) and MLOps pipelines
• Experience deploying models into real-time or near real-time production systems
• Knowledge of semiconductor, chemicals, energy, or advanced manufacturing processes