Staff Manufacturing Engineer

ZT Systems•Secaucus, NJ

6d•$93,000 - $136,400

About The Position

The Staff Manufacturing Engineer leads the development, applied research, and implementation of industrial models, business analytics solutions, and analytical tools that establish manufacturing performance targets and support business decision-making. The role works with manufacturing leadership and cross-functional teams to analyze operational challenges and evaluate improvement opportunities, informing changes to resources, infrastructure, and manufacturing processes through insights from key analytical tools. The position leads the Industrial and BI Engineering team and owns the ongoing development and improvement of analytical models, data structures, dashboards, and decision-support tools. It applies statistical analysis, optimization, simulation, and data mining to manufacturing processes and operational challenges, and leads analytical projects from problem definition through implementation. The role also provides technical guidance to junior team members and communicates complex findings as actionable recommendations for stakeholders.

Requirements

Bachelor’s degree in industrial engineering, mechanical engineering, or a related field with 2 years of experience; alternatively, a master’s or doctoral degree with any amount of experience.
Relevant experience in an engineering role and experience in manufacturing environments.
Experience with business intelligence and analysis tools such as SQL, Python, Tableau, and Power BI.
Experience with Microsoft Office tools, including Outlook, Excel, and Word.
Fluency in mathematical computing with at least one programming language, such as Python, R, or Java, and ability to learn technical methods and tools independently.
Hands-on experience building, running, and analyzing simulations; designing analytical models; and applying statistical analysis and optimization techniques.
Experience applying analytical methods, statistical analysis, and data-driven decision-making to improve manufacturing performance and business outcomes.
Experience working with cross-functional teams to solve complex operational and business challenges using data analytics.
Ability to simplify results and statistical concepts into clear, actionable visualizations and presentations.
Ability to communicate complex, data-driven topics as clear overviews and insights.
Strong analytical and problem-solving capabilities, effective communication, independent initiative, and ability to complete projects with urgency in a fast-paced environment.
Growth mindset and dedication to continuous learning.

Nice To Haves

Exposure to business, finance, or economics.

Responsibilities

Lead the Industrial and BI Engineering team in turning data into insights and business decisions that guide the manufacturing organization.
Develop, maintain, and continuously improve industrial models, analytical tools, data structures, and business intelligence solutions.
Investigate and evaluate new technologies and data-driven approaches in automation, modern manufacturing, business intelligence, and advanced analytics.
Identify and lead enterprise-level Kaizen and continuous improvement projects targeting cycle time, cost, capacity utilization, throughput, and on-time delivery.
Serve as the focal point for engineering analysis related to changes in technology, AI, products, manufacturing processes, and systems.
Own and enhance business intelligence models and decision-support tools for short-term and long-range planning, ensuring accuracy and actionable insights.
Conduct statistical and business analyses with SQL, Python, and business intelligence tools to access, analyze, manipulate, and visualize complex data.
Lead cross-functional analytical projects from problem definition through solution implementation, and communicate recommendations to stakeholders.
Provide technical leadership and guidance to junior team members on complex analytics, modeling, and continuous improvement projects.
Apply statistical, analytical, optimization, and simulation methodologies to manufacturing processes and operational challenges.
Conduct applied research and analytical evaluations of manufacturing processes, production metrics, and business challenges to identify improvement opportunities.
Develop, validate, and improve predictive and prescriptive models, dashboards, and decision-support solutions for capacity planning, resource optimization, cost analysis, forecasting, and performance improvement.
Collaborate with Manufacturing, Supply Chain, Operations, Engineering, and Finance to translate analytical findings into recommendations for resource planning, technology investments, process improvements, and operational decisions.
Evaluate emerging analytical methodologies, statistical techniques, optimization approaches, and business intelligence technologies for manufacturing innovation and continuous improvement.