
Data Science Engineer 1 AI Development
- Lynchburg, VA
- Permanent
- Full-time
processes, and advancing Al applications in welding manufacturing operations. The ideal candidate will have expertise in data science, machine learning, and industrial process optimization, with a focus on leveraging Al to enhance welding efficiency, quality and predictive maintenance.Key Responsibilities:
- Data infrastructure and warehousing
- Design, develop, and maintain data warehouses to store and manage large-scale welding operations data
- Ensure seamless integration of data from various sources, including sensors, PLCs, and production databases
- Implement ETL (Extract, Transform, Load) processes for efficient data extraction and processingProcess Monitoring & Optimization:
- Develop and deploy real-time monitoring systems to track key welding parameters to detect and report anomalies
- Analyze sensor and machine data to improve weld quality, defect detection, and predictive maintenance
- Utilize statistical models, Al, and ML techniques to enhance process control and automationAl and Machine Learning in Welding Operations:
- Develop Al-driven solutions to predict weld defects, optimize settings, andimprove process consistency
- Implement computer vision techniques for weld quality inspection and automation
- Leverage deep learning and reinforcement learning to optimize robotic welding systemsData Analytics & Reporting:
- Create dashboards and visualization tools to provide insights into production efficiency and quality
- Perform exploratory data analysis and present findings to engineers, production teams, and leadership
- Assist in root cause analysis by correlating welding data with production outcomesCollaboration and Continuous Improvement:
- Work closely with welding engineers, automation specialists, and IT teams to enhance manufacturing intelligence
- Remain updated on emerging Al and data science technologies relevant to industrial welding
- Provide guidance on best practices for data governance, security and complianceRequired Qualifications:- Bachelor's degree in Data Science, Computer Science, Engineering, Statistics or related field- Must be a US citizen with no dual citizenship- Must be able to obtain and maintain a U.S. Department of Energy (DOE) clearance CVAA,Technical Skills:- Proficiency in Python, R, SQL and big data technologies- Knowledge or experience with machine learning frameworks (TensorFlow, PyTorch, Scikit-learn) - Knowledge of industrial loT and sensor data processing- Familiarity with data warehousing tools, e.g., Microsoft SQL Server- Knowledge or experience with predictive modeling, time-series forecasting, and anomaly detection- Knowledge or experience in computer vision for industrial applications is a plus- Dashboard creation with Power BI would be a plus- Knowledge of web technologies (for example REST APIs) and frameworks (e.g. React, Vue.js) would be a plus- Knowledge of C# would be a plusIndustry Knowledge & Experience:- Understanding of welding processes, materials, and quality control (preferred, but not mandatory)- Previous experience in manufacturing, automation, or industrial analytics is highly desirable, but not mandatory- Knowledge of Six Sigma, Lean Manufacturing, or process optimization techniques is a plusSoft skills:- Strong problem-solving skills with an analytical mindset- Ability to communicate complex data insights to technical and non-technical stakeholders- A team player with a collaborative approach to interdisciplinary projects- Must possess proven ability and willingness to learn new tools- Must be able to demonstrate superior written, oral, and interpersonal communication skills- Must be highly self-motivated and directed, with keen attention to detailPreferred Qualifications:- Certifications in data science, Al, or cloud computing (AWS Certified Machine Learning, Google Professional Data Engineer, etc.)- Master's in Data Science Preferred- Experience with robotic and mechanized welding systems and automation
- Understanding of edge computing for real-time data processing