Senior Scientist , Computational Biology

Giga Gen is advancing transformative antibody drugs for immune deficiency, infectious diseases, and checkpoint-resistant cancers by leveraging industry-leading, single-cell technologies. Giga Gen'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.

Giga Gen'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.

Giga Gen is leveraging its proprietary technology platforms for the continued discovery of novel recombinant polyclonal drugs and monoclonal antibodies to treat life-threatening diseases.

Giga Gen, a subsidiary of Grifols, seeks 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.

• 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.

• Education 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.
• Prior industry experience is preferred.
• 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.
• 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.
• 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.

Occupational Demands:
Work is performed in an office environment with exposure to electrical office equipment. Frequently sits for 6-8 hours per day. Frequent hand…