Senior Scientist I, Computational Biology

Grifols•San Francisco, CA

3d•Onsite

About The Position

GigaGen, a subsidiary of Grifols, is seeking a talented and highly motivated Senior Scientist, Computational Biology to support antibody therapeutics discovery, technology development, and machine learning efforts. This is a broad computational biology role that combines supporting diverse bioinformatics and sequencing needs across research teams with independently leading computational research and technology development projects. The ideal candidate is a versatile scientist who enjoys solving biological problems, collaborating closely with experimental scientists, and applying modern computational and machine learning approaches where they can meaningfully advance the science. GigaGen is advancing transformative antibody drugs for immune deficiency, infectious diseases, and checkpoint-resistant cancers by leveraging industry-leading, single-cell technologies. GigaGen's novel technology platforms uniquely capture and recreate complete immune repertoires as functional antibody libraries. This approach has enabled the creation of first-in-class recombinant polyclonal antibody therapies for the treatment of infectious diseases. GigaGen's lead oncology asset, GIGA-564, is an anti-CTLA-4 monoclonal antibody that has demonstrated improved anti-tumor efficacy in vivo through a unique mechanism of action. GigaGen is leveraging its proprietary technology platforms for the continued discovery of novel recombinant polyclonal drugs and monoclonal antibodies to treat life-threatening diseases.

Requirements

PhD or Master's degree with significant relevant experience in Bioinformatics, Computational Biology, Genomics, Biology, Immunology, or a related discipline.
A minimum of 5+ years of experience.
Strong experience analyzing next-generation sequencing datasets and developing bioinformatics workflows.
Strong proficiency in Python and/or R and in a Unix/Linux command-line environment, including shell scripting and common bioinformatics tools.
Strong quantitative reasoning, statistical analysis, data visualization, and scientific interpretation skills.
Experience applying machine learning to biological data, including supervised learning, model training and evaluation, and interpretation of model performance.
Experience with protein language models, sequence embeddings, fine-tuning, and other modern approaches for modeling biological sequences.
Demonstrated ability to independently formulate computational approaches, critically evaluate and validate results, troubleshoot problems, and drive projects to completion with strong attention to data quality and reproducibility.
Excellent communication and collaboration skills, including the ability to work effectively with experimental and multidisciplinary teams, manage multiple priorities, and proactively communicate progress, timelines, and challenges.

Nice To Haves

Prior industry experience is preferred.
Experience or interest in applying computational approaches across diverse areas of drug discovery and technology development, such as target and antibody discovery, structural biology, proteomics, and high-throughput screening.

Responsibilities

Analyze and critically interpret diverse biological datasets, including next-generation, long-read, single-cell, and antibody repertoire sequencing data.
Provide computational expertise across research teams to address a broad range of bioinformatics and computational biology needs.
Independently lead computational research and technology development projects, from defining scientific questions and strategy through analysis, interpretation, and experimental validation.
Apply machine learning approaches to biological datasets, including predictive modeling and modern deep learning approaches for biological sequences.
Evaluate and apply protein language models and other biological foundation models to address questions in antibody discovery and development.
Develop robust, reproducible computational workflows for processing, analyzing, integrating, and visualizing biological data.
Collaborate closely with computational and experimental scientists to design studies, interpret results, troubleshoot unexpected findings, and guide subsequent experiments.
Clearly and proactively communicate project plans, progress, challenges, results, and recommendations to collaborators and stakeholders.