Manufacturing AI & Analytics Architect

Role Description

The Manufacturing AI & Analytics Architect will guide the design and delivery of AI, advanced analytics, data architecture, and decision-support solutions for BWXT production manufacturing environments. This role will partner with various teams to translate operational challenges into secure, scalable, supportable solutions that improve quality, throughput, downtime, process visibility, and data-driven decision making.

Your Day to Day as a Manufacturing AI & Analytics Architect

• Partner with manufacturing and business-unit leaders to identify, prioritize, and define high-value AI and analytics use cases across production, quality, maintenance, supply chain, safety, and operational performance.
• Translate manufacturing workflows and operational pain points into technical requirements, data requirements, solution architectures, implementation plans, and measurable success criteria.
• Design practical solutions such as dashboards, alerts, predictive models, anomaly detection, optimization tools, AI assistants, data products, APIs, workflow automation, and lightweight applications.
• Analyze data from ERP, MES, SCADA, historians, PLCs, sensors, quality systems, maintenance systems, production logs, engineering systems, spreadsheets, and other operational sources.
• Develop reusable data models, features, semantic definitions, KPIs, and architecture patterns that can scale across plants, lines, stations, processes, and business units.
• Partner with data engineering, software engineering, application, platform, and cybersecurity teams to move prototypes into production-ready solutions.
• Support data governance, metadata, data quality, security, regulatory compliance, lifecycle management, and stewardship practices for manufacturing data and analytics.
• Apply MLOps and production support practices such as version control, testing, model deployment, monitoring, drift detection, retraining, logging, and documentation.
• Communicate technical concepts, assumptions, limitations, risks, and recommendations clearly to plant teams, engineers, leaders, and non-technical stakeholders.
• Challenge assumptions constructively, clarifies ambiguous requirements, and helps prioritize solutions that provide practical business value.

Qualifications

• Bachelor’s degree in Data Science, Computer Science, Information Systems, Statistics, Industrial Engineering, Manufacturing Engineering, Engineering, Operations Research, Applied Mathematics, or a related field.
• Minimum of 10 years of relevant experience, including some experience in data architecture, analytics, AI/ML, data engineering, manufacturing systems, and/or solution architecture.
• Experience designing or delivering analytics, AI/ML, data, BI, automation, or decision-support solutions in a professional environment.
• Strong SQL skills and experience working with large, complex datasets.
• Python or similar analytical programming experience using tools such as pandas, NumPy, scikit-learn, PyTorch, TensorFlow, XGBoost, statsmodels, or similar libraries.
• Experience with supervised and unsupervised analytics or machine learning methods, such as classification, regression, clustering, anomaly detection, time-series analysis, forecasting, optimization, or statistical process analysis.
• Experience working with cloud-based data and analytics platforms such as Azure, AWS, GCP, Databricks, Snowflake, Fabric, or similar environments.
• Strong understanding of data architecture, including data modeling, data integration, metadata, data quality, data governance, and data lifecycle management.
• Experience communicating technical findings in business-friendly language and produce clear technical architecture and support documentation.
• Highly self-motivated, collaborative, detail-oriented, and results-driven.
• Must be a U.S. citizen.
• Must be able to obtain and maintain a U.S. Department of Energy (DOE) clearance.

Preferred Additional Qualifications

• Experience applying AI, analytics, data science, or data architecture in manufacturing, industrial, nuclear, aerospace, defense, semiconductor, quality, maintenance, supply chain, or operations environments.
• Experience with manufacturing systems such as MES, SCADA, PLCs, historians, CMMS/EAM, QMS, ERP, industrial IoT platforms, engineering systems, or production scheduling systems.
• Understanding of manufacturing KPIs such as throughput, cycle time, downtime, OEE, scrap, rework, takt time, bottlenecks, quality escapes, first-pass yield, schedule adherence, and safety events.
• Experience with Azure, Azure AI/OpenAI, Databricks, Power BI, SQL Server, Python, APIs, Power Platform, containerized applications, or similar enterprise platforms.
• Experience with real-time or near-real-time anomaly detection, streaming data, event-driven architectures, MQTT, Kafka, Spark, Azure Event Hubs, Azure IoT, or similar technologies.
• Experience building dashboards, internal applications, or decision-support tools using Power BI, Tableau, Grafana, Streamlit, Dash, FastAPI, or similar tools.
• Experience in regulated, high-security, safety-conscious, export-controlled, or mission-critical environments.

Benefits

• Competitive salary and benefits package, including health, dental, and retirement plans.
• Flexible work schedules and paid time off to promote a healthy work-life balance.
• Professional development opportunities, including mentorship programs and sponsorship for continuing education.
• An inclusive atmosphere that celebrates new perspectives and supports collaboration between different generations.
• The chance to be part of a mission-driven organization making a positive impact on the future of energy.
• Opportunities for continuous learning and training to grow throughout your career!