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Supervisor, Customer Support

Job in Irvine, Orange County, California, 92713, USA
Listing for: Western Digital
Full Time position
Listed on 2026-03-07
Job specializations:
  • IT/Tech
    Data Analyst, Data Science Manager, Technical Support, AI Engineer
Salary/Wage Range or Industry Benchmark: 80000 - 100000 USD Yearly USD 80000.00 100000.00 YEAR
Job Description & How to Apply Below

Company Description

At Western Digital, our vision is to power global innovation and push the boundaries of technology to make what you thought was once impossible, possible.

At our core, Western Digital is a company of problem solvers. People achieve extraordinary things given the right technology. For decades, we’ve been doing just that—our technology helped people put a man on the moon and capture the first-ever picture of a black hole.

We offer an expansive portfolio of technologies, HDDs, and platforms for business, creative professionals, and consumers alike under our Western Digital®, WD®, and ™.

We are a key partner to some of the largest and highest-growth organizations in the world. From enabling systems to make cities safer and more connected, to powering the data centers behind many of the world’s biggest companies and hyperscale cloud providers, to meeting the massive and ever-growing data storage needs of the AI era, Western Digital is fueling a brighter, smarter future.

Today’s exceptional challenges require your unique skills. Together, we can build the future of data storage.

Job Description

The Customer Service Supervisor maintains a high bar for service quality and customer satisfaction across our global contact centers and service centers. This role helps define and execute the Global Customer Support strategy across all countries and languages Western Digital supports. Has a strong focus on using AI, analytics, and automation to scale quality, knowledge, and efficiency. You will own service level management, escalations and quality programs for internal teams and third‑party vendors, foster continuous education, and partner across functions to deliver world‑class customer support.

ESSENTIAL DUTIES AND RESPONSIBILITIES
  • Day‑to‑day operations:
    Assist in managing daily call center operations and workforce planning across multiple regions and 10+ languages, ensuring consistent SLAs and a seamless omnichannel experience (voice, chat, email, social, self‑service).
  • Training and enablement:
    Maintain, design, and deliver training for GCSS agents, including new hire, customer service, social media. Review and maintain and update internal knowledge base to ensure accuracy and consistency.
  • Global vendor management:
    Partner with third‑party vendors to meet KPIs related to quality, customer satisfaction, and efficiency; drive corrective actions and continuous improvement plans.
  • Social and product launches:
    Facilitate new social support programs and product introductions, ensuring teams and bots are trained and ready before launch.
  • Quality and compliance:
    Conduct quality audits across channels and regions. Ensure adherence to best practices, privacy and data handling standards, and regional regulations.
  • Escalations:
    Support direct customer engagement for internal and external escalations, driving timely, empathetic, and complete resolutions.
  • Governance and communication:
    Lead recurring operational reviews and cross‑functional calls; attend QBRs as required; surface insights and recommendations to leadership.
  • Travel:
    Up to 5%, including international travel as needed.
AI Integration / Responsibilities
  • AI strategy in support:
    Help define and operationalize the AI roadmap for customer support, including virtual agents, agent assist/copilots, knowledge retrieval, and intelligent routing.
  • Virtual agent excellence:
    Partner with product and engineering to design, test, and iterate chatbots and IVR/NLU flows; improve intent recognition, containment rate, and handoff quality.
  • Agent assist and knowledge:
    Deploy and maintain AI‑powered tools that summarize contacts, suggest next best actions, generate responses, and surface knowledge; maintain prompt libraries and guardrails.
  • Quality automation:
    Leverage speech and text analytics, auto‑QA, and sentiment analysis to increase coverage and consistency of evaluations; coach teams using data‑driven insights.
  • Forecasting and WFM:
    Use machine learning–driven forecasting, capacity models, and real‑time analytics to optimize staffing and service levels.
  • Experimentation and measurement:
    Run A/B tests on scripts, flows, deflection experiences, and knowledge content; track…
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