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

Position Summary

• - Lead design, implementation, and evolution of scientific data products and integration strategies for AI-enabled drug discovery and development.
• - Create scalable, interoperable, AI-ready data products connecting discovery, preclinical, clinical, safety, and real-world evidence.
• - Establish data architecture, integration strategy, metadata framework, and productization approach for semantic reasoning, knowledge graphs/GraphRAG, advanced analytics, and agentic AI.

Key Responsibilities

• - Define and execute scientific data product strategy across discovery research, translational science, preclinical safety, clinical development, pharmacovigilance, and real-world evidence.
• - Design integration/harmonization frameworks (SEND, SDTM, ADaM, MedDRA, imaging, omics, biomarker, pathology, real-world data).
• - Lead AWS-deployed implementation strategy; ensure alignment with enterprise architecture, security, governance, and AI readiness.
• - Develop curated/semantic-ready datasets, feature stores, metadata products, scientific data services, and AI-ready assets; define onboarding/transformation/validation/publication patterns.
• - Define metadata/data quality standards; implement lineage/provenance/traceability and FAIR principles.
• - Build predictive AIML models to support translational safety decision-making.
• - Partner with scientific and technical stakeholders to translate scientific questions into scalable solutions.

Required Qualifications

• - Master’s or PhD in CS, Data Engineering, Bioinformatics, Biomedical Informatics, Information Systems, Computational Biology, or related discipline.
• - 5+ years in scientific data engineering/architecture/products or life sciences informatics.
• - Experience designing enterprise-scale scientific data products; supporting drug discovery/development, clinical research, or pharmacovigilance.
• - Experience developing predictive models in those domains.

Preferred Qualifications

• - Knowledge graphs/semantic architectures/GraphRAG; AI-ready data products/feature stores; ontology-driven integration.
• - Partnering with cloud providers/external platform teams; regulated scientific environments.

Benefits (time off)

• - Vacation (120 hrs/yr); Sick time (40 hrs/yr; CO 48; WA 56); Holiday pay incl. floating (13 days/yr); Work/Personal/Family Time (up to 40 hrs/yr); Parental Leave (480 hrs/1 year); Bereavement Leave (240 hrs immediate family; extended 40 hrs/yr); Caregiver Leave (80 hrs/52-week period); Volunteer Leave (32 hrs/yr); Military Spouse Time-Off (80 hrs/yr).