This position is located onsite in San Diego, CA.
Looking for a dynamic and innovative scientist to spearhead the development and enhancement of bioinformatics systems that drive the discovery and design of novel biologics. The ideal candidate will bring experience in next-generation sequencing (NGS) technologies and bioinformatics tools for biotherapeutics, with additional expertise in computational protein design being highly desirable. This role offers an exciting opportunity to collaborate with multidisciplinary teams to address complex challenges in biologics discovery and design.
Responsibilities:
- Create and optimize bioinformatics systems to analyze NGS datasets, integrating them with Benchling to support the discovery and design of protein therapeutics with specific binding and biological properties.
- Use bioinformatics to scale antibody screening workflows, including data curation, analysis, interpretation, and process automation.
- Work closely with a multidisciplinary team to identify, characterize, and optimize antibodies and related biologics, contributing to the therapeutic pipeline.
- Lead the design and refinement of data models in Benchling to improve data entry, organization, and accessibility.
- Share strategies, findings, and conclusions within the Antibody Engineering Team and across other teams to drive efficiencies and facilitate feedback.
- Strengthen inter-team collaboration by enhancing communication, operations, and data systems.
Qualifications:
- Proficiency in setting up GIT repositories and working within collaborative programming environments.
- Strong skills in Python, C++, or similar programming languages.
- Experience designing and building custom hardware for big data and machine learning applications.
- BS/BM in biochemistry, biophysics, computer science, engineering, or related field with 5+ years of bioinformatics experience or PhD in similar fields with 2+ years of academic or industry bioinformatics experience.
- Proven experience developing bioinformatics tools for NGS data analysis.
- Familiarity with SQL and Benchling, or other laboratory information management systems.
- Strong understanding of antibody sequences, structures, and binding kinetics.
- Expertise in computational protein design.
- Experience with machine learning and AI approaches for biotherapeutic design and optimization.
