| Job Description: |
Role Type
Domain & Technology SME with experience in Lab Data exposure, data integration & data enablement
Reporting / Positioning
Reporting to Global Head of Laboratories within IT Operations and Labs
Works closely with Client Lab Operations, Quality Assurance, Quality Control, IT, Data Engineering, and Architecture teams
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Role Objective
The Pharma Labs Data SME is responsible for enabling end to end lab data exposure and data integration from laboratory instruments and platforms into enterprise data, analytics, and AI ecosystems in a GxP compliant manner.
The role provides lab domain expertise combined with data and integration knowledge, ensuring laboratory data is trusted, traceable, analytics ready, and aligned to Connected Lab / Lab 4.0 transformation initiatives.
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Key Responsibilities
1. Lab Data Enablement & Exposure
• Act as the single point SME for exposing laboratory data from:
o Lab instruments
o LIMS, ELN, LES, SDMS, CDS platforms
• Define and govern data extraction, ingestion, and exposure patterns (batch / near real time).
• Support integration of lab data into:
o Enterprise Data Platforms
o Analytics and reporting layers
o AI / GenAI use cases
2. Instrument & Platform Integration
• Provide expertise on instrument connectivity and data flows, including:
o Instrument APIs, parsers, middleware, schedulers, SDMS
• Guide teams on standardized integration approaches across lab landscapes.
• Support harmonization across QC, QA, R&D, and Manufacturing labs.
3. Data Modelling & Contextualization
• Define lab data models and semantic structures aligned to:
o Test, method, specification, sample, batch, and material context
• Ensure correct mapping of raw instrument data → results → business context.
• Support master data alignment and metadata management.
4. GxP Compliance & Data Integrity
• Ensure all lab data exposure adheres compliance rules including e.g. GxP
• Partner with QA and Validation teams on data related activities
5. Analytics, Digital & AI Enablement
• Support enablement of lab data consumption for e.g.:
o Advanced analytics & dashboards
o Statistical Process Control (SPC)
o Continuous Process Verification (CPV)
o AI / GenAI use cases (e.g., deviations intelligence, anomaly detection)
• Work with client data engineers and data scientists to ensure data usability and quality
6. Client & Stakeholder Collaboration
• Act as a trusted advisor to client lab, quality, and IT stakeholders
• Translate lab business needs into clear data and integration requirements
• Support data related workshops, design sessions, and solution walkthroughs
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Required Skills & Experience
Domain Expertise
• Strong experience in pharma laboratory environments e.g. QC, QA, R&D, Manufacturing labs
• Solid understanding of Lab workflows, Instrument data generation, result lifecycle and quality processes
Technical & Data Skills
• Hands on experience with:
o LIMS, ELN, LES, SDMS, CDS
o Instrument data integration and exposure
• Good understanding of:
o Data pipelines, APIs, and integration architectures
o Structured and unstructured lab data
o Metadata and semantic models
• Exposure to cloud data platforms and analytics ecosystems is an advantage.
Compliance & Quality
• Strong knowledge of GxP and data integrity principles.
• Experience supporting validation, audits, and inspections.
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Nice to Have
• Experience with Connected Lab / Lab 4.0 initiatives
• Exposure to ISA 95 / ISA 88 concepts
• Experience supporting GenAI / RAG based lab data use cases
• Consulting or transformation program experience in large pharma environments
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Typical Profile
• Experience: 8–15+ years in pharma labs data roles
• Proven experience working in global, multi site pharma programs
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Value Delivered to Client
• Reliable, compliant lab data exposure across platforms and instruments
• Reduced manual data handling and reconciliation
• Faster access to lab insights for quality, operations, and AI use cases
• Strong alignment between lab operations, quality, IT, and data teams
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