Senior Quality & Digital QMS Lead

EY

Woodbridge Township (NJ)

Remote

USD 97,000 - 203,000

Full time

17 hours ago

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Benefits offered by this job

Medical and dental coverage

Pension and 401(k)

Paid time off

Job summary

EY is hiring a Senior in Supply Chain Manufacturing Quality to help pharmaceutical, biotech and medical-device clients connect QA/QC, lab, and manufacturing with deployable digital solutions. You will lead digital quality workflows, support validation and audits, and coordinate across QA, QC, IT, and engineering teams.

Your role includes designing data models, integrating systems, and contributing to AI-enabled quality initiatives with governance and traceability.

Qualifications

Bachelor’s degree in life sciences, quality, engineering, information systems, computer science, supply chain, or related discipline.
At least 3 years of relevant experience in Life Sciences quality or related field.
Experience with quality, laboratory, or manufacturing processes in regulated environments.
Ability to travel 80-100% as needed.
Authorized to work in the U.S. without sponsorship.

Responsibilities

Assess quality and lab workflows, user needs, system interfaces and data pain points.
Document processes, user stories, requirements, data mappings and acceptance criteria for digital quality solutions.
Support design and implementation of deviations, investigations, CAPA, change control, audits and release quality.
Design and improve digital-quality workflows using eQMS/QMS platforms and integrations.
Connect QA/QC and lab processes across LIMS, eQMS/QMS, MES, ERP and other systems.
Develop data models, mappings, data-quality rules, and contextualized datasets.
Work with architects on interfaces, APIs, and data movement across environments.
Prepare functional/integration test scripts and maintain traceability to requirements and risks.
Support deployment readiness, training, and handover with clear documentation.
Collaborate with quality and validation teams on GxP, data integrity, and electronic records.
Integrate data across quality, lab, manufacturing signals to improve decisions.
Contribute to governed AI applications for quality with human-in-the-loop governance.
Communicate progress, risks, and next steps to client counterparts and leads.