Principal Scientist, Data Science (Data Products, Integration & Analysis)

Position Summary

The Principal Scientific Data Scientist will lead the design, implementation, and evolution of scientific data products and integration strategies supporting AI-enabled drug discovery and development. This individual will be responsible for creating scalable, interoperable, and AI-ready data products that connect discovery, preclinical, clinical, safety, and real-world evidence domains, and enable the creation of validated-biomarker data assets. The role will establish the data architecture, integration strategy, metadata framework, and productization approach needed to support semantic reasoning, knowledge graphs, GraphRAG, advanced analytics, and agentic AI applications.

Mission

Build and operationalize AI-ready scientific data products that enable seamless integration, harmonization, and reuse of data across the drug discovery and development lifecycle.

Key Responsibilities

• Define and execute a scientific data product strategy supporting Discovery Research, Translational Science, Preclinical Safety, Clinical Development, Pharmacovigilance, and Real-World Evidence.
• Establish reusable, scalable data products that support analytics, AI, knowledge graph, and scientific reasoning use cases.
• Develop product roadmaps aligned with organizational priorities and scientific objectives.
• Design integration frameworks connecting heterogeneous scientific data sources.
• Define data harmonization strategies spanning SEND, SDTM, ADaM, MedDRA, Imaging, Omics, Biomarker, Pathology, and Real-world data.
• Create architecture patterns supporting cross-domain data interoperability.
• Define the implementation strategy for scientific data products deployed on AWS.
• Partner with Amazon engineering and deployed platform resources to deliver scalable data pipelines and data products.
• Provide technical leadership and architectural oversight for implementation activities aligned with recommendations from the Data Strategy group.
• Ensure digital solutions align with enterprise architecture, security, governance, and AI-readiness requirements.
• Lead design and implementation of curated datasets, semantic-ready data products, feature stores, metadata products, scientific data services, and AI-ready data assets.
• Establish reusable patterns for data onboarding, transformation, validation, and publication.
• Define metadata standards and data quality frameworks.
• Implement lineage, provenance, traceability, and FAIR data principles.
• Establish monitoring and quality controls for scientific data products.
• Build predictive AI/ML models to support translational safety decision making.
• Partner with Discovery Scientists, Toxicologists, Clinical Scientists, Safety Scientists, Data Scientists, Knowledge Architects, and AI Engineers.
• Translate scientific questions into scalable data products and technical solutions.

Required Qualifications

• Master’s or PhD in Computer Science, Data Engineering, Bioinformatics, Biomedical Informatics, Information Systems, Computational Biology, or related scientific discipline.
• 5+ years of experience in scientific data engineering, data architecture, data products, or life sciences informatics.
• Demonstrated experience designing and delivering enterprise-scale scientific data products.
• Experience supporting drug discovery, development, clinical research, or pharmacovigilance organizations.
• Experience developing predictive models in drug discovery, development, clinical research, or pharmacovigilance organizations.
• Strong expertise in data architecture, data modeling, data product design, cloud-native data platforms, metadata management, data governance, and predictive model development.
• Experience with AWS-based data platforms, data lakes and lakehouses, distributed data processing, APIs and data services, and data cataloging and lineage solutions.
• Strong familiarity with SEND, SDTM, ADaM, and MedDRA.

Preferred Qualifications

• Familiarity with FHIR, OMOP, DICOM, and biomarker and omics data standards.
• Experience supporting knowledge graphs, semantic architectures, or GraphRAG initiatives.
• Experience building AI-ready data products and feature stores.
• Familiarity with ontology-driven data integration approaches.
• Experience partnering with cloud providers or external platform teams.
• Experience operating in highly regulated scientific environments.
• Strategic thinker capable of defining long-term data product roadmaps.
• Strong communicator who can bridge scientific and technical communities.
• Ability to influence cross-functional teams without direct authority.
• Strong execution focus with a bias toward scalable, reusable solutions.