Senior Life Sciences Manufacturing & AI Transformation Lead

EY

Cincinnati (OH)

Remote

USD 97,000 - 178,000

Full time

4 days ago

Be an early applicant

A complete application in a minute

Tailored resume and cover letter, ready to send.

Get past ATS filters

Job summary

EY is seeking a Senior Life Sciences Manufacturing Specialist to design, test, and deploy digital solutions for regulated plant operations. You will connect production, quality, lab, engineering, and supply chain needs with deployable technology across the lifecycle.

In this client-facing role, you’ll own technical deliverables, resolve issues, and translate shop-floor learnings into actionable requirements.

Qualifications

Bachelor’s degree in engineering, life sciences, manufacturing, information systems, computer science, supply chain, or related discipline.
At least 3 years of relevant Life Sciences manufacturing or related experience.
Experience in pharma/biotech/medical-device or regulated environments.
Experience producing requirements, process maps, data mappings, interfaces, or deployment materials.
Experience collaborating with operational and technical delivery teams.
Ability to travel 80-100%.

Responsibilities

Assess manufacturing and quality workflows, user needs, system interfaces, equipment data, and current-state pain points.
Document process flows, user stories, functional requirements, data mappings, interface requirements, controls, and acceptance criteria.
Support design and implementation of MES/MOM, eDHRs, recipe management, batch genealogy, and plant performance reporting.
Trace information across MES/MOM, ERP, LIMS, QMS, historians, SCADA, DCS/PLC, and asset-management platforms.
Develop and review equipment and batch hierarchies, data-quality rules, and contextualized datasets.
Work with architects on interface specifications, APIs, and data movement between plant, edge, and enterprise.
Configure or prototype solution components and document design decisions.
Prepare and execute functional and integration test scripts; manage defects and traceability.
Support deployment readiness, training, and handover with clear technical documentation.
Collaborate with client quality and validation teams on GxP, data integrity, and computer-system validation.
Contribute to analytics and AI use cases by assessing source data quality and manufacturing context.
Communicate progress, risks, and next steps to client counterparts and leads.