Sr. Engineer, Industrial AI and Analytics
Listed on 2026-07-21
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IT/Tech
AI Business & Operations, AI Engineer (Applied/Software)
Overview
Do you want to join a team that is changing the world? Do you have a strong background as a Sr. Engineer, Industrial AI and Analytics? Then we’re looking for you! Put your skills to meaningful use, gain unique experience, and work with world‑class team members with diverse backgrounds and expertise who share the same vision. Join the PECNA team today!
ResponsibilitiesSr. Engineer, Industrial AI and Analytics
Meet the Recruiter:
Princess Damato
Job Summary:
Panasonic Energy is seeking a Sr. Engineer, Industrial AI and Analytics reporting to the Sr. Director, Next Gen Initiatives. This role operates within the Next Gen Initiatives (NGI) team, a strategic function responsible for advancing automation, AI enablement, and operational transformation across PECNA. The role focuses on the digital and systems execution layer of manufacturing: deploying analytics, industrial AI, data products, and decision‑support tools that improve how manufacturing teams see, decide, and act.
The engineer will drive deployment of factory intelligence platforms, operational dashboards, AI/ML solutions, digital twin/simulation concepts, command‑center capabilities, and AI‑enabled decision support. This role converts NGI concepts into production‑ready digital capabilities that improve safety, quality, productivity, throughput, and cost performance. The ideal candidate blends future‑forward curiosity with a hands‑on approach to problem‑solving. You will translate complex manufacturing challenges into structured initiatives, driving cross‑functional execution from early discovery and ideation through proof‑of‑concept and full deployment, ensuring measurable performance improvements and sustained adoption across the shop floor.
Duties
- Partner with site leadership and manufacturing stakeholders to define and prioritize analytics, digital AI, and production systems initiatives aligned to NGI pillars and identify opportunities to improve performance, cost, and workforce capability.
- Translate operational challenges into clear initiative charters and drive end‑to‑end delivery across digital systems (analytics/AI).
- Own use cases from discovery through proof of concept to scaled deployment, ensuring solutions are operationally relevant, explainable, and embedded into frontline workflows.
- Manage initiative timelines, risks, and dependencies; drive issue resolution with cross‑functional stakeholders.
- Partner with site leadership and manufacturing stakeholders to define and prioritize analytics, digital AI, and production systems initiatives aligned to NGI pillars and identify opportunities to improve performance, cost, and workforce capability.
- Translate operational challenges into clear initiative charters and drive end‑to‑end delivery across digital systems (analytics/AI).
- Own use cases from discovery through proof of concept to scaled deployment, ensuring solutions are operationally relevant, explainable, and embedded into frontline workflows.
- Manage initiative timelines, risks, and dependencies; drive issue resolution with cross‑functional stakeholders.
- Evaluate, pilot, deploy emerging analytics and digital AI technologies across production sites, including industrial data platforms, AI/ML solutions, digital twins, command centers, and agentic/GenAI tools. Identify and evaluate high‑impact opportunities in areas such as predictive maintenance, quality prediction, SPC automation, process optimization, yield improvement, and decision support.
- Identify and prioritize analytics and digital AI opportunities that deliver measurable improvements in productivity, throughput, quality, and cost performance.
- Partner with Operations, Process Engineering, Quality, Maintenance, IT/OT, SCADA/MES, and data teams to convert manufacturing data into reliable, governed data products, dashboards, and decision‑support tools.
- Develop evaluation frameworks to assess readiness, data quality, usability, security, reliability, scalability, and ROI before scaled deployment.
- Bridge the gap between Operations, Engineering, Safety, Maintenance, DataX, Quality, Finance, HR, and…
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