Associate Scientist – Digital Discovery: Antibody Bioinformatics 

Let’s do this. Let’s change the world.

In this vital role, you will join a collaborative Digital Discovery team of scientists who combine experimental biology with bioinformatics and data analysis to improve how biologics discovery data are generated, captured, analyzed, and reused. This bench-proximal computational role is designed for a scientist with hands-on wet-lab or experimental training and demonstrated experience or training in bioinformatics, computational biology, or biological data analysis. 

Working alongside experimental and computational colleagues, you will help turn complex biological data into reproducible, analysis-ready information that supports discovery decisions. You will apply biological context to data quality and interpretation, develop or adapt practical analytical tools, and strengthen the connection between laboratory workflows, biological data, and downstream analysis.

Bioinformatics & Data Analysis

• Run established antibody discovery computational workflows, assess sequencing and data quality, and support downstream analysis of antibody repertoires and candidate sequences. 

• Analyze BCR repertoire, NGS, single-cell, protein, and associated antibody discovery datasets to characterize sequence diversity, identify antibody candidates, and support discovery decisions, with guidance from senior team members as appropriate. 

• Develop R and/or Python scripts, analytical tools, reports, and visualizations to automate downstream BCR-Seq analyses and enable scientists to explore, interpret, and communicate discovery results. 

Biological Data Integration & Curation

• Organize, curate, and integrate BCR-Seq, antibody screening, and associated experimental data and metadata to support downstream analysis, data mining, and reuse across antibody discovery programs. 

• Work with wet-lab scientists to ensure sample, sequencing, and antibody discovery data are captured, connected, and structured to support downstream analysis and traceability, including within platforms such as Benchling. 

Experimental Partnership & Team Collaboration

• Apply hands-on knowledge of molecular biology, immunology, biologics discovery, genomics, NGS, and related laboratory workflows to interpret data within its experimental context. 

• Partner with wet-lab and computational scientists to investigate data-quality issues, improve experimental-to-computational handoffs, and help ensure experiments generate robust, analysis-ready data. 

• Communicate analytical findings and recommendations through clear visualizations, concise scientific summaries, and collaborative problem-solving. 

Master’s degree and 1+ year of relevant experience 

OR 

Bachelor’s degree and 3+ years of relevant experience 

in Biological Sciences, Biochemistry, Molecular Biology, Immunology, Bioinformatics, Computational Biology, or a related field. 

• Hands-on wet-lab or experimental research experience in molecular biology, immunology, biologics or antibody discovery, genomics, NGS, protein characterization, or a related life-science discipline. 

• Demonstrated training or experience in bioinformatics, computational biology, scientific informatics, or biological data analysis through coursework, research projects, internships, or professional experience. 

• Working knowledge of R and/or Python for biological data analysis, visualization, and scripting; experience with SQL is a plus. 

• Experience working with complex experimental datasets and associated metadata, including data curation, integration, quality control, and documentation. 

• Exposure to NGS, immune repertoire, single-cell, flow cytometry, or other high-dimensional biological datasets; familiarity with tools such as Cell Ranger, Seurat/Scanpy, FlowJo, Bioconductor, or comparable platforms is a plus. 

• Familiarity with Linux/command-line environments, reproducible analysis practices, cloud/HPC environments, and/or laboratory informatics systems such as Benchling, ELNs, or LIMS. 

• Strong communication, organization, and collaboration skills, with an interest in working across experimental and computational disciplines.