Principal Scientist, Data Science; Data Products, Integration & Analysis

Position Summary

• Lead design, implementation, and evolution of scientific data products and integration strategies supporting AI-enabled drug discovery and development.
• Create scalable, interoperable, AI-ready data products connecting discovery, preclinical, clinical, safety, and real-world evidence; enable validated-biomarker data assets.
• Establish data architecture, integration strategy, metadata framework, and productization approach for semantic reasoning, knowledge graphs, GraphRAG, advanced analytics, and agentic AI.
• Partner with scientific stakeholders, knowledge architects, AI engineers, and platform teams to define the future-state scientific data ecosystem and deliver high-quality products.

• Build and operationalize AI-ready scientific data products for seamless integration, harmonization, and reuse across the drug discovery/development lifecycle.

• Define and execute data product strategy across discovery, translational science, preclinical safety, clinical development, pharmacovigilance, and real-world evidence.
• Design data integration/harmonization (SEND, SDTM, ADaM, MedDRA, imaging, omics, biomarkers, pathology, real-world data).
• Lead AWS deployment strategy; deliver scalable pipelines/products; ensure security/governance/AI-readiness alignment.
• Develop curated datasets, semantic-ready products, feature stores, metadata products, scientific data services, and AI-ready assets.
• Define metadata standards and quality frameworks; implement lineage/provenance/traceability and FAIR principles; monitor quality controls.
• Build predictive AI/ML models for translational safety decision making.

• Master's or PhD in CS, Data Engineering, Bioinformatics, Biomedical Informatics, Information Systems, Computational Biology, or related discipline.
• 5+ years experience in scientific data engineering/architecture/products or life sciences informatics; enterprise-scale product delivery.
• Experience in drug discovery/development/clinical research/pharmacovigilance and building predictive models in these domains.

• Data architecture, modeling, product design, cloud-native platforms, metadata management, data governance, predictive model development.
• AWS platforms; data lakes/lake houses; distributed processing; APIs/data services; data cataloging/lineage solutions.
• Familiarity with SEND/SDTM/ADaM/MedDRA.

• Knowledge graphs/semantic architectures/GraphRAG; AI-ready products and feature stores; ontology-driven integration; experience with cloud/external platform teams; regulated scientific environments.
• Preferred familiarity: FHIR, OMOP, DICOM, biomarker/omics standards.
• Preferred skills:
  Advanced Analytics, Critical Thinking, Data Analysis, Data Quality, Data Science, Data Visualization, Digital Fluency, Process Improvements, Strategic Thinking, Workflow Analysis.

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