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ZT Systems

Designs and builds hyperscale server hardware

Staff Manufacturing Engineer

Data Science +1Confirmed live in the last 24 hours
Full-timeSecaucus, NJ, USAIn-person

About the Job

Requirements

  • A bachelor's degree in Industrial Engineering, Mechanical Engineering, or a related field with at least two years of experience, or 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 the ability to learn technical methods and tools independently.
  • Fluency in data visualization and presentation, including the ability to simplify results and statistical concepts into actionable insights.
  • 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.

Responsibilities

  • Lead the Industrial and Business Intelligence Engineering team in turning data into information, insights, and business decisions that steer the manufacturing organization.
  • Develop, maintain, and continuously improve industrial models, analytical tools, data structures, and business intelligence solutions that support operational and strategic decision-making.
  • Work with the Industrial Engineering team to investigate, evaluate, and propose new technologies and data-driven solutions in automation, modern manufacturing, business intelligence, and advanced analytics.
  • Identify and drive enterprise-level Kaizen and continuous improvement projects using statistical analysis, optimization techniques, and structured problem-solving methodologies targeting production metrics such as cycle time, cost, capacity utilization, throughput, and on-time delivery.
  • Serve as the focal point for engineering analysis related to changes in technology, artificial intelligence, products, manufacturing processes, and systems, applying statistical analysis, analytical modeling, and evidence-based recommendations.
  • Own and continuously enhance the business intelligence suite of analytical models and decision-support tools for short-term and long-range planning, ensuring model accuracy, continuous improvement, and actionable business insights.
  • Conduct statistical and business analyses with multiple departments, using SQL, Python, and business intelligence tools to access, analyze, manipulate, and visualize complex multivariate data.
  • Lead cross-functional analytical projects from problem definition through solution implementation by identifying business needs, developing analytical strategies, and communicating insights and recommendations to stakeholders.
  • Provide technical leadership and guidance to junior team members on complex analytics, modeling, and continuous improvement projects while promoting data-driven decision-making and analytical best practices.
  • Evaluate and apply advanced statistical, analytical, optimization, and simulation methodologies to manufacturing processes and operational challenges, using quantitative analysis to recommend improvements to manufacturing performance, operational efficiency, and strategic decision-making.
  • Conduct applied research and analytical evaluations of manufacturing processes, production metrics, and business challenges by investigating trends, evaluating operational performance, and identifying improvement opportunities through data-driven analysis.
  • Develop, validate, and continuously improve predictive and prescriptive analytical models, dashboards, and decision-support solutions using statistical analysis and data mining techniques to support capacity planning, resource optimization, cost analysis, operational forecasting, and manufacturing performance improvement.
  • Collaborate with Manufacturing, Supply Chain, Operations, Engineering, and Finance teams to translate analytical findings into recommendations for resource planning, technology investments, process improvements, and operational decision-making.
  • Investigate and evaluate emerging analytical methodologies, statistical techniques, optimization approaches, and business intelligence technologies to identify opportunities for innovation and continuous improvement in manufacturing operations.

Desired Qualifications

  • Exposure to business, finance, or economics is advantageous.

About the Company

ZT Systems

Designs and builds hyperscale server hardware

ZT Systems designs and builds specialized server and storage hardware for large data centers and hyperscale cloud providers. It creates complete rack-scale compute and storage solutions tailored to customers, combining servers, storage, power, cooling, and management for scalable infrastructure. The company differentiates itself through deep hands-on engineering and close customer collaboration to deliver customized, purpose-built hardware rather than off-the-shelf components. Its goal is to help leading cloud providers run reliable, scalable infrastructure, a strategy that AMD leveraged in 2025 by acquiring ZT Systems to integrate its design and engineering expertise into broader AI and data-center solutions.

Company size
1,001-5,000
Company stage
Acquired
Total funding
Not listed
Headquarters
Secaucus, NJ
Founded
1994

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