Staff Industrial Engineer

connect; Secaucus (NJ); On-site; USD 110,000 - 170,000; Full time

Job summary

connect is seeking a Sr. Manufacturing Engineer to lead the Industrial and BI Engineering team, turning data into insights that drive manufacturing decisions.

You will develop, maintain, and expand analytical models, tools, and data structures to support operation planning and performance measurement. You will collaborate with Manufacturing, Supply Chain, Operations, Engineering, and Finance to translate findings into actionable recommendations, applying statistics, optimization, and data-driven

Qualifications

• Bachelor's Degree in Industrial Engineering, Mechanical Engineering, or related field.
• 2 years of experience; master's or doctoral degree with any amount of experience accepted.
• Experience in manufacturing environments is preferred.
• Proficiency with SQL, Python, Tableau and Power BI.
• Strong analytical and problem-solving abilities with good communication.
• Exposure to business, finance, or economics is advantageous.

Responsibilities

• Lead the Industrial and BI Engineering team to turn data into insights for business decisions.
• Develop, maintain, and improve industrial models, analytical tools, data structures, and BI solutions.
• Investigate and propose new technologies and data-driven solutions in automation and analytics.
• Apply statistical analysis, optimization, and problem-solving to drive continuous improvement.
• Collaborate with cross-functional teams to translate analytics into actionable recommendations.

Education

Bachelor's Degree in Industrial Engineering or Mechanical Engineering

Tools

SQL

Python

Tableau

Power BI

Excel

Job description

About the role:

The Sr. Manufacturing Engineer is responsible for leading the development, applied research, and implementation of industrial models, business analytics solutions, and analytical tools that set manufacturing performance targets and support business decision-making The Sr. Manufacturing Engineer works with manufacturing leadership and cross-functional teams to analyze operational challenges and evaluate improvement opportunities to define the roadmap for changes to resources, infrastructure, and manufacturing processes driven by meaningful insights derived from the organization’s key analytical tools.

What you'll do:

Lead the Industrial and BI Engineering team in turning data into information, information into insights, and insights into key business decisions that steer the direction of the manufacturing organization.

Drive efforts in developing, maintaining, and continuously improving the effectiveness and scope of 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 the use of 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 by applying statistical analysis, optimization techniques, and structured problem-solving methodologies that target key production metrics such as cycle time, cost, capacity utilization, throughput and on time delivery while influencing behaviors/decisions at several levels of the organization.

Serve as the focal point for engineering analysis related to changes in technology, AI, products, manufacturing processes, and systems by 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.

Interface with multiple departments to conduct statistical and business analyses that drive valuable business insights, 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 in the execution of 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 develop evidence-based recommendations to improve 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 and evidence-based 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 cross-functional teams, including Manufacturing, Supply Chain, Operations, Engineering, and Finance, to translate complex analytical findings into actionable recommendations that support 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.

What you'll bring:

Bachelor's Degree in Industrial Engineering, Mechanical Engineering, or related field, with 2 years of experience. Alternatively, Master's or Doctoral degree with any amount Of experience accepted.

Exposure to business, finance, or economics is advantageous.

Relevant experience in an engineering role. Experience in manufacturing environments.

Experience with business intelligence and analysis tools such as SQL, Python, Tableau, and Power Bl.

Experience with Microsoft Office tools (Outlook, Excel, and Word).

Exceptional analytical capabilities, thrives in a fast-paced environment, loves problem-solving, is a good communicator, and is passionate about enabling the future of