Lead Process Manager, Supply Chain (Hybrid)

Target; Minneapolis (MN); Hybrid; USD 95,000 - 171,000; Full time

Benefits offered by this job

Health benefits including medical, etc

401(k)

Employee discounts

Paid sick leave

Paid national holidays

Paid vacation

Job summary

Target is seeking a Lead Process Manager in Supply Chain Operations to drive performance improvements, reduce losses, and scale Standard Work across sites. You will translate observations into validated changes in methods, staffing, and routines while balancing safety, quality, delivery, and cost.

The role requires collaboration with field leaders and HQ partners. A core focus is identifying waste, delivering cost savings, and sustaining improvements through data-driven experimentation and

Qualifications

• Bachelor’s degree or equivalent experience.
• 3+ years of experience in distribution, fulfillment, manufacturing, logistics, or process improvement.
• Experience with process observation, time analysis, work sampling, cycle time data.

Responsibilities

• Identify material operational losses and design improvements.
• Lead problem solving using root cause analysis and data.
• Develop and test process experiments and pilots.
• Mentor leaders and partners to deploy Standard Work.
• Collaborate with HQ and field to address safety, quality, and productivity.

Skills

Process improvement

Root cause analysis

Kaizen

Value Stream Mapping

Standardized Work

Data analysis

Problem solving

Strong communication

Travel readiness

Education

Bachelor’s degree or equivalent experience

Job description

Target is seeking a Lead Process Manager in Supply Chain Operations to drive performance improvements, reduce losses, and scale Standard Work across sites. You will translate observations into validated changes in methods, staffing, and routines while balancing safety, quality, delivery, and cost.

The role requires collaboration with field leaders and HQ partners. A core focus is identifying waste, delivering cost savings, and sustaining improvements through data-driven experimentation and