R&D Data Scientist

You will provide critical advisory on which data attributes, metadata standards, ontologies, and master data models must be controlled, standardized, and harmonized at R&D specific levels. Reporting into the R&D organization and closely partnering with Enterprise Data Office (EDO), Operations, and IT, you will drive foundational data readiness across key R&D scientific platforms—including ARCHI, Benchling (ELN / Registry / Inventory), Revvity (Signals / Informatics), and Clinical Systems (EDC, CTMS, eCOA/ePRO).

Strategic Priorities

• R&D Governance & Systems Policy: Support the interoperability of Data across R&D and Enterprise-wide ecosystems.
• Attribute-Level Data Architecture: Determine and advise on critical R&D data attributes, controlled vocabularies, schemas, and master data entities required to ensure cross-system interoperability.
• Pipeline Acceleration & AI Readiness: Optimize data capture, extraction, and standardization across preclinical and clinical workflows to power machine learning, generative AI, and predictive modeling.
• Data Quality & Compliance Excellence: Establish automated data quality frameworks, lineage monitoring, and compliance controls (FAIR principles, GxP, ALCOA+) across interconnected scientific systems.

Responsibilities

• R&D Data Governance & Policy Formulation: Author, establish, and govern enterprise-wide data policies, standards, data classification, and operating procedures across R&D software platforms.
• Attribute-Level Control & Metadata Management: Evaluate data models across scientific applications to define what must be controlled at the enterprise level versus local system configurations; establish standardized ontologies and controlled vocabularies.
• R&D Systems Integration & Data Strategy: Serve as the lead data science advisor for core discovery and development platforms including ARCHI, Benchling, Revvity, and Clinical Systems.
• End-to-End Data Science & Modeling: Design, build, and deploy machine learning and statistical models that leverage harmonized data assets to solve complex R&D challenges.
• Data Quality Frameworks & Automated Monitoring: Implement automated data validation, anomaly detection, and data completeness metrics at ingestion points across enterprise endpoints.
• Cross-Functional Influence & Stakeholder Management: Partner with senior stakeholders across Discovery, Translational Medicine, Clinical Operations, Regulatory, Quality Assurance, and IT.
• Collaborate on Infrastructure & Deployment: Work alongside Data Engineers, ML Engineers, and Cloud Solution Architects (Azure / GCP) to design scalable data pipelines, data mesh/lakehouse architectures, and secure data access layers.
• Champion FAIR Data & Life Science Compliance: Embed FAIR data principles and ensure data governance workflows meet regulatory standards (GxP, 21 CFR Part 11, ALCOA+ data integrity guidelines).