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Tier-1 employer but specialized senior scientific data role reduces broad applicant competition.
Role requires biotech-specific scientific data and domain knowledge, limiting cross-industry transferability.
Explicit degree-and-years alternatives plus domain-specific skills enforce strict, non-negotiable filters.
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Design, maintain, and optimize scalable scientific data models and ML-ready datasets that enable machine learning, analytics, and research workflows in Large Molecule Discovery.
Implement metadata, data lineage, governance, and transformation pipelines to improve data quality, traceability, and reuse across multiple scientific systems.
Collaborate cross-functionally with scientists, AI/ML researchers, software engineers, and data platform teams to accelerate AI-enabled drug discovery research.
Doctorate degree with 5+ years relevant experience, or Master’s degree with 8+ years, or Bachelor’s degree with 10+ years relevant experience.
Proficiency in SQL, Python, and modern data engineering platforms is required.
Experience designing data models for scientific, analytical, or machine learning applications.
Work Experience Required: PhD with 5+ years OR Master’s with 8+ years OR Bachelor’s with 10+ years in directly related roles.
Experienced in building and managing complex biological and experimental datasets to support ML and analytics in a pharmaceutical or biotechnology research environment.
Demonstrates strong cross-disciplinary collaboration with scientists and AI/ML teams to translate experimental workflows into structured, reusable data assets.
Skilled in implementing data governance, metadata frameworks, and reproducible data pipelines within large-scale scientific data ecosystems.