Senior Quality & Supply Chain – Digital QMS Lead

EY

Baltimore (MD)

Remote

USD 97,000 - 195,000

Full time

8 hours ago

Be an early applicant

A complete application in a minute

Tailored resume and cover letter, ready to send.

Get past ATS filters

Benefits offered by this job

Medical and dental coverage

401(k) and retirement plans

Generous paid time off

Job summary

EY seeks a Senior in Supply Chain Manufacturing Quality to connect QA/QC, laboratory, and manufacturing with deployable technology. You will drive digital quality solutions across the lifecycle, from requirements through validation, deployment, and stabilization.

You will collaborate with QA, QC, regulatory, IT, and data teams, own deliverables, and guide architectures and interfaces for secure data movement. The role emphasizes AI‑enabled quality workflows with governance and human oversight.

Qualifications

Bachelor's degree in life sciences, quality, engineering, information systems, computer science, supply chain, or a related discipline.
At least 3 years of relevant experience in Life Sciences quality, Quality Assurance, Quality Control, laboratory systems, digital QMS, systems implementation, technology consulting, or a related field.
Experience working with quality, laboratory, or manufacturing processes in a pharmaceutical, biotechnology, medical‑device, or comparably regulated environment.
Experience with one or more core quality processes, such as deviations, investigations, CAPA, change control, complaints, audits, supplier quality, batch disposition, release, or quality risk management.

Responsibilities

Assess quality and laboratory workflows, user needs, system interfaces, instrument and equipment data, and current‑state pain points through interviews, workshops, process observation, and data review.
Document process flows, user stories, functional requirements, data mappings, interface requirements, quality‑system controls, and acceptance criteria for digital quality solutions.
Support the design and implementation of quality‑domain capabilities spanning deviations and nonconformances, investigations, CAPA, change control, complaints, audits and inspection readiness, supplier quality, batch disposition and release, review by exception, and quality risk management.
Design and improve digital‑quality workflows using eQMS/QMS platforms, including workflow configuration, records management, approvals, escalation paths, effectiveness checks, reporting, and integration with adjacent enterprise systems.
Connect QA/QC and laboratory processes across LIMS, eQMS/QMS, MES, ERP, chromatography and other laboratory data systems, and relevant manufacturing platforms; support assays, stability programs, OOS/OOT traceability, deviation and CAPA analysis, and batch‑review workflows.
Develop and review quality and laboratory data models, source‑to‑target mappings, data‑quality rules, and contextualized datasets that relate quality events to products, materials, batches, methods, specifications, instruments, equipment, and process conditions.
Work with solution architects and engineers on interface specifications, APIs, laboratory‑instrument connectivity, and the secure movement of quality, laboratory, manufacturing, equipment, and process data across site and enterprise environments.
Configure or prototype solution components where appropriate to your experience and the engagement, and document design decisions for review by quality, technical, and client leads.
Prepare and execute functional and integration test scripts, record defects, support resolution and retesting, and maintain traceability between requirements, risks, controls, and test evidence.
Support deployment readiness, controlled change, user training, hyper‑care, and handover by preparing clear documentation and responding to issues from quality and laboratory users.
Partner with client quality and validation teams to prepare or review documentation addressing GxP/GMP, data integrity, electronic records and signatures, controlled change, validation or risk‑based assurance, audit trails, and end‑to‑end traceability.
Integrate and contextualize quality, laboratory, manufacturing, equipment, and process data to identify recurring events, detect trends, support root‑cause analysis, and improve quality decision‑making.
Contribute to governed AI applications for Quality, including AI‑assisted investigations and root‑cause analysis, anomaly and trend detection, predictive quality, and decision support, with appropriate human‑in‑the‑loop review, governance, and controls.
Communicate progress, risks, dependencies, and recommended next steps to client counterparts and engagement leaders; coordinate defined tasks with junior colleagues when needed.