Sr Operations Analyst

Milwaukee Tool•Milwaukee, WI

29d

About The Position

The Senior Operations Analyst plays a critical role in transforming operations data into actionable insights that drive performance, traceability, quality, and continuous improvement across global operations. This role combines expertise in data analytics, manufacturing systems, and process engineering to support New Product Development (NPD) and ongoing production and/or quality initiatives. The analyst will contribute to development of data pipelines, building analytical models, and partnering with global teams to improve operational visibility, yield, and quality. Specific duties include: Manufacturing Data & Analytics Lead analytical efforts to uncover process trends, detect anomalies, and enable data-driven decision making across production lines. Design and implement dashboards and reporting tools to monitor key manufacturing KPIs such as yield, throughput, cycle time, and error frequency. Apply statistical methods, SQL, and Python-based analytics to quantify process capability, variation, and equipment performance. Support NPD launch readiness by building analytical baselines and monitoring process stability during ramp-up. Translate complex datasets into clear visualizations and insights to guide improvement activities. Traceability & Systems Integration Support end-to-end traceability solutions across new and existing production lines—linking component, process, and test data. Ensure robust data governance practices are followed for all operational systems. Support rollout of data acquisition and visualization tools in collaboration with global quality and manufacturing sites. Quality & Service Analytics Develop predictive models for defects, warranty claims, and service trends to drive proactive improvements. Collaborate with quality and service teams to analyze feedback loops and enhance product reliability. Monitor compliance with quality standards and provide insights to reduce rework, scrap, and service costs.

Requirements

Ability to travel up to 20–30% (domestic and international) to support data implementation and line development.
Hands-on approach with manufacturing equipment, data collection hardware, and industrial systems.
Curiosity and agility to adapt to evolving data architectures and advanced manufacturing technologies.
Bachelor’s degree in Engineering, Data Science, or related field required.
Strong proficiency in SQL, with working knowledge of Python, Power BI, and modern data platforms (e.g., Spark, Databricks) preferred.
Demonstrated ability to analyze large, complex datasets and develop actionable insights.
Knowledge of statistical analysis, process control, and manufacturing metrics.
Understanding of manufacturing process data, automation systems, and traceability architectures (e.g., MES, SCADA, PLC data capture).

Responsibilities

Lead analytical efforts to uncover process trends, detect anomalies, and enable data-driven decision making across production lines.
Design and implement dashboards and reporting tools to monitor key manufacturing KPIs such as yield, throughput, cycle time, and error frequency.
Apply statistical methods, SQL, and Python-based analytics to quantify process capability, variation, and equipment performance.
Support NPD launch readiness by building analytical baselines and monitoring process stability during ramp-up.
Translate complex datasets into clear visualizations and insights to guide improvement activities.
Support end-to-end traceability solutions across new and existing production lines—linking component, process, and test data.
Ensure robust data governance practices are followed for all operational systems.
Support rollout of data acquisition and visualization tools in collaboration with global quality and manufacturing sites.
Develop predictive models for defects, warranty claims, and service trends to drive proactive improvements.
Collaborate with quality and service teams to analyze feedback loops and enhance product reliability.
Monitor compliance with quality standards and provide insights to reduce rework, scrap, and service costs.