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Tier-1 employer and metro location increase applicant density, but senior niche scientific data skills moderate competition.
Role requires life-science and scientific-dataset expertise, so cross-industry transferability is limited.
Explicit multi-tiered years requirements plus domain-specific scientific data and biotech experience make filters strict.
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Design, manage, and optimize scientific datasets to support AI/ML applications in Large Molecule Discovery.
Develop and maintain scalable data models, metadata frameworks, and transformation pipelines for machine learning and scientific research workflows.
Collaborate cross-functionally with scientists, AI/ML researchers, engineers, and data platform teams to enable accessible, high-quality, and reusable discovery data.
PhD with 5+ years relevant experience OR Master's with 8+ years OR Bachelor's with 10+ years in related fields.
Proficiency in SQL, Python, and modern data engineering platforms.
Experience in designing data models and creating ML-ready datasets for scientific or analytical applications.
Work Experience Required: At least 5 years with PhD, or 8+ years with Master’s, or 10+ years with Bachelor’s, as stated above.
Experienced in handling large-scale biotechnology, pharmaceutical, genomics, or life sciences datasets.
Strong at bridging scientific workflows with data engineering to create reusable, scalable data assets for AI/ML.
Familiar with metadata standards, data lineage, governance, and implementing reproducible data pipelines in research informatics environments.