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Senior Data Scientist, Computational Biology
4 months ago
- Advancing BigHat therapeutic programs through ownership of the entire data science life cycle from experimental design through data analysis and modeling, including implementation of novel antibody design strategies, development of robust data pipelines for assays of antibody function and developability, and data-driven improvements to our statistical and ML models
- Developing and implementing robust and reliable products (pipelines, models, and visualizations) for quantifying antibody function and developability, from screening stage through lead candidate promotion
- Pushing forward our scientific knowledge of antibody function, structure, diversity, and developability, assessing the reliability of new and existing datasets, metrics, and models, and qualifying them with experimental data from our high-throughput platform
- Collaborating closely with BigHat protein scientists, program leads, software engineers, ML scientists, and other team members to enable rapid, iterative molecule development
- PhD in a relevant domain [Immunology, Computational Biology, Structural Biology, Bioengineering, Bioinformatics, or similar] with 3+ years of industry experience preferred
- Extensive experience with and knowledge of immunology, antibody structure, antibody developability (liability motifs, stability, aggregation, immunogenicity, etc.), antibody diversity (e.g.sequencing and analysis of immune repertoires), and/or pharmacokinetics/pharmacodynamics
- Proficient in Python, R, or similar programming languages.
- Proficient with cloud computing infrastructure like AWS and with version control like Github or Gitlab
- Excellent communication skills, experience interacting with diverse scientific teams.
- Enjoys a fast paced environment and executing across multiple projects.
- The experience and judgment to operate/make key technical decisions independently
- Nice-to-haves include:
- Experience in therapeutic antibody development
- Experience with data from phage or yeast display or NGS generally.
- Experience with antibody structure modeling and/or embeddings or representations of antibody sequence
- Experience with technologies for measuring antibody diversity and selection, including sequencing data of immune repertoires
- Experience building interactive data visualizations and dashboards for high throughput biological data.
