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Operations Specialist

USA-WI-Neenah · Senior · Full-time

No sponsorship$80k-$94kDetected 3 days ago
Data ScienceExcelSAPSupply ChainLogisticsProcurementLeadershipCommunicationCollaborationProblem SolvingMentoring

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odds of building a lasting career here

53Sponsors, lottery-bound
Cap-exempt (no lottery)0
Sponsors this role100
Entry-level history0
PERM / green-card track0
Lottery odds (Level II)66
Fits your clock70

Sponsors, but it's cap-subject — you still face the weighted lottery (~30% per draw at Level II). Good if you win; have a cap-exempt backup on your list.

Lottery odds assume a STEM candidate.

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Employer immigration record

from this employer's Department of Labor filings

Green-card filing pattern in this occupation

Context, not a finding about this posting: of this employer's 169 green-card filings in this occupation, 99% were for a worker who already held the job.

Green-card follow-through: 88%

Of 120 labor certifications old enough to have been used, 15 expired without the employer filing the next step. Median time from filing to decision: 493 days.99% of their filings were for a worker who already held the job.Only certifications past the 180-day window are counted — recent ones cannot have expired yet.

Reported a layoff in this occupation

In a green-card filing decided 2026-06-17, this employer stated it had laid off workers in Software Developers (CA) during the preceding six months. 3 such filings on record.Describes the period before that filing, not necessarily today. Unlike WARN notices this is occupation-specific and federal.

Files H-1B transfers

233 transfer filings in the last year, covering 1907 workers. Median labor-condition decision: 7 days. An employer that already files transfers is one that can take over an existing H-1B.

Sourced from Department of Labor LCA, PERM and prevailing-wage disclosure data. Employer matching is by name, so figures may be split across an employer's legal entities. Absence of a filing means none appears in our copy of the data, not that none exists.

Community outcomes

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About the role

  • As a person, you're a problem-solver - a connector - someone who thrives on creating order from complexity and driving continuous improvement.
  • You see the big picture while mastering the details, ensuring that every product, process, and partnership flows with precision and purpose.
  • You live your life in alignment with the highest values of integrity, efficiency, and collaboration, always working to turn today's challenges into tomorrow's success stories.

Responsibilities

  • In this role, you'll help us deliver better care for billions of people around the world.
  • As a member of the Central Stores team, collaborate with manufacturing, engineering and stores functions to:
  • Support the training and development of Stores personnel on stores practices and procedures as needed.
  • Make recommendations of appropriate stocking levels of spare parts to optimize inventory.
  • Identify alternative solutions for spare parts to; replace obsolete parts, reduce costs, improve reliability and/or optimize inventory.
  • Additionally, the role provides significant support to the Kimtech and Global/Segment procurement teams.
  • Develop and deliver equipment and processes that meet safety requirements, policies and guidelines.

Requirements

  • A bachelor's or advanced degree in Engineering, Manufacturing, Supply Chain, Logistics Data Science, or demonstrated experience in a related field
  • SAP Subject Matter Expertise level knowledge and capability.
  • 5+ years' experience in a variety of operational assignments in a manufacturing, logistics, maintenance and/or staff environment.
  • Perform in cross-functional teams to achieve objectives within required timeframes.
  • Experience working with external technical service providers, parts/equipment suppliers.
  • Ability to lead small cross-functional projects to achieve intended results within required timeframes.
  • Must have the ability to translate technical knowledge into consumable data for the non-technical experts.
  • Strong analytical skills.
  • A demonstrated ability with Data Science and/or Analytical connected tools including, Access, Excel, & VBA.
  • Ability to work collaboratively with peers, internal customers, and external customers.
  • Strong communication skills across multiple levels of the organization, including senior leadership. Must have the ability to translate technical knowledge into consumable data for the non-technical experts.
  • Ability to travel to support business needs (up to 10% of the time).

Skills

  • Inventory Health / Inventory Management.
  • Inventory Performance Reporting and Analytics.
  • Export Compliance.
  • Internal Control Compliance.
  • Repair/Warranty Cost Savings.
  • Training
  • Technical Subject Matter Expertise.
  • Coaching/Mentoring.
  • Problem Solving / Development.

Compensation

  • 79,880 - 94,440 USD
  • At Kimberly-Clark, pay is just one aspect of our total rewards package, which also includes a variety of benefits and opportunities to achieve, thrive and grow.
  • Along with base pay, this position offers eligibility for a target bonus and a comprehensive benefits suite, including our 401(k) and Profit Sharing plan.
  • The anticipated base pay range for this role is provided above for a fully qualified hire.
  • Actual pay will depend on several factors, such as location, role, skills, performance, and experience.
  • Please note that the stated pay range applies to US locations only.

Company info

  • For a complete overview, see www.mykcbenefits.com ​ Flex That Works at Kimberly-Clark We believe great work happens when people come together with purpose.
  • That's why we offer a flexible work model that blends remote work with intentional in-person collaboration - helping you connect, grow, and innovate while maintaining the balance you value.

Visa & Work Authorization

  • This role is available for local candidates already authorized to work in the role's country only.

This listing is sourced directly from Linkedin's careers page and normalized into a canonical job model.