Manufacturing Data Scientist
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At a glance
Job overview
TriMas Corporation is hiring a Manufacturing Data Scientist. PennAero is seeking a Manufacturing Data Scientist to transform complex operational data into actionable insights, improving productivity, quality, cost, reliability, and supply-chain performance. This role involves partnering with various teams to develop analytical solutions that support data-driven decision-making. The ideal candidate has strong expertise in Python and SQL, experience with ERP systems, and a practical understanding of manufacturing processes and data.
Key focus areas include Analyze manufacturing, production, quality, maintenance, inventory, and supply-chain data to identify trends, risks, inefficiencies, and improvement opportunities., Build, validate, and maintain data pipelines and reusable analytical datasets using SQL and/or Python., and Develop predictive and prescriptive models for applications such as equipment reliability, predictive maintenance, quality forecasting, yield optimization, demand planning, inventory optimization, and production scheduling..
Important skills include Python, SQL, Manufacturing Processes, Data Analysis, Data Pipeline, and Predictive Modeling.
Skills & qualifications
Skills
Qualifications
Full job description
About PennAero:
PennAero is a leading manufacturer of highly engineered fasteners and specialized components for critical aerospace, defense, space, and advanced energy applications. We partner with customers to solve their most complex challenges, bringing technical depth and disciplined, agile execution when it matters most. Experience guides our growth—strengthening capabilities and expanding our global platform as markets evolve. To learn more about PennAero's capabilities and commitment to aerospace excellence, visit https://pennaero.com
Position Overview
We are seeking a Manufacturing Data Scientist to transform complex operational data into actionable insights that improve productivity, quality, cost, reliability, and supply-chain performance. This role will partner with manufacturing, engineering, quality, supply chain, finance, and information technology teams to develop analytical solutions that support data-driven decision-making across the organization.
The ideal candidate has strong expertise in Python and SQL, experience working with enterprise resource planning systems, and a practical understanding of manufacturing processes and data. This individual must be comfortable working with large, complex datasets and translating analytical findings into clear recommendations for technical and nontechnical stakeholders.
Key Responsibilities
Analyze manufacturing, production, quality, maintenance, inventory, and supply-chain data to identify trends, risks, inefficiencies, and improvement opportunities.
Build, validate, and maintain data pipelines and reusable analytical datasets using SQL and / or Python
Develop predictive and prescriptive models for applications such as equipment reliability, predictive maintenance, quality forecasting, yield optimization, demand planning, inventory optimization, and production scheduling.
Extract, clean, reconcile, and integrate data from ERP systems, MES, quality systems, equipment sensors, HCM systems, and other operational sources
Partner with manufacturing engineers, plant leaders, quality teams, supply-chain professionals, and business stakeholders to define analytical requirements and measurable success criteria.
Create dashboards, reports, and data visualizations that communicate operational performance and model results clearly.
Conduct root-cause analyses related to production losses, downtime, scrap, rework, throughput, cycle time, and process variation.
Develop and monitor key performance indicators, including overall equipment effectiveness (OEE), first-pass yield, schedule attainment, capacity utilization, downtime, scrap rate, and inventory accuracy.
Deploy analytical models and establish processes for monitoring model performance, data quality, and business impact.
Document data sources, methodologies, assumptions, model limitations, and technical processes.
Promote data literacy and analytical best practices across manufacturing and operations teams.
Ensure analytical solutions comply with applicable data governance, security, quality, and regulatory requirements.
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