Director, Scientific Data & Informatics
D
Drug HunterScientific intelligence
United States. Boston, Massachusetts, United StatesFull-TimeDirector
Salary not disclosed
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Job Details
- Experience
- 10+ years working with scientific data in drug discovery, biotech, or life science data/informatics companies, including 3+ years leading people or a data function
- Required Skills
- PythonSQLData modeling
Requirements
- PhD in cheminformatics, bioinformatics, computational biology, structural biology, or a related quantitative life-science field.
- 10+ years working with scientific data in drug discovery, biotech, or life science data/informatics companies.
- 3+ years leading people or a data function.
- Working cheminformatics literacy, including molecular representations, structure standardization, and substructure/similarity search concepts.
- Experience with RDKit or similar tools.
- Fluency in bioinformatics, including working knowledge of biological databases and ontologies, target and pathway biology, and gene/protein identifier mapping.
- Strong data modeling skills and hands-on proficiency in Python and SQL.
- Experience with data pipelines and modern data tooling.
- Understanding of the drug discovery process from target to clinic.
- Ability to work across scientific details and engineering/product trade-offs.
Responsibilities
- Serve as the primary internal subject-matter expert for cheminformatics and scientific data across chemistry and biology.
- Own the strategy for acquiring, representing, integrating, normalizing, and maintaining scientific data.
- Define how chemical entities are represented, registered, standardized, and resolved across sources.
- Establish curation standards, controlled vocabularies, QA rules, and tooling for scalable data curation.
- Define approaches for extracting structured scientific information from literature and patents and linking it to canonical entities.
- Own entity resolution and ontology strategy across scientific concepts and handle ambiguity and conflicting source assertions.
- Evaluate scientific data sources across chemistry, biology, DMPK, drug, and clinical domains.
- Establish standards and processes for data quality, provenance, validation, completeness, and freshness.
- Partner with Science, Engineering, and Product Management on data models, technical approaches, specifications, and trade-offs.
- Lead, mentor, hire, and manage the Data team and translate scientific data priorities into executable projects.
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