The Opportunity
Visterra is seeking a Scientist to join our Genomics Platform team, which directly supports target identification, indication prioritization and supporting progression of drug candidates in clinical development. The team uses single-cell multi-omics, spatial transcriptomics, and human genetics to characterize human disease in kidney, autoimmune, and neurodegenerative indications.
The Scientist will own projects end to end, from initial QC to biological interpretations and insights. This involves framing the question with program teams, shaping experimental designs for omics data generation, analyzing internally generated and public human datasets, and presenting the answer to stakeholders, including collaborating teams and senior management.
This is a full-time position located at Visterra's offices in Waltham, MA.
About the Genomics Platform team
The Genomics Platform team is composed of six researchers, three computational biologists and three experimental researchers. The two subgroups work as one team, designing experiments together and analyzing results together, so computational scientists shape what data gets generated rather than receive it afterward.
We focus mostly on human data. We generate paired single-cell and spatial transcriptomics data from human tissue in-house and combine them with public disease atlases and population genetics resources. We are members of the FNIH Accelerating Medicines Partnership in Autoimmune and Immune-Mediated Diseases (AMP AIM), which adds disease-tissue single-cell and spatial data in rheumatoid arthritis, lupus, Sjögren's disease, systemic sclerosis, and psoriatic disease. On the genetics side, we have built an internal platform that moves from GWAS summary statistics to prioritized target genes through fine-mapping, colocalization, gene-level association, and single-cell integration.
We work in AWS and treat agentic AI as a core part of how a small team covers a lot of ground. Every computational scientist runs analyses with Claude Code, and we are actively building agentic workflows that take a project from data acquisition through analysis to target reasoning. We also maintain several academic collaborations and aim to publish.
How you will have an impact
- Find new targets. Analyze human disease tissue, including AMP AIM datasets across autoimmune diseases, to identify pathogenic cell states and the molecules that hold them in place, then partner with a discovery team as they develop biologics against the target you proposed.
- Decide where a drug goes. Integrate genetic, single-cell, and spatial evidence across diseases to prioritize indications for targets already in the pipeline.
- Explore new mechanisms of neurodegenerative diseases. Integrate genetic evidence across diseases and pathways, and explore immune mechanisms in neurodegenerative disease, as a source of new targets.
- Build the platform. Extend the internal genetics platform with new modules and tools, and help build the agentic workflows the team runs it with.
Responsibilities
- Own projects from question to recommendation: quality control, analysis, interpretation, and presentation.
- Integrate population-level genetic data such as GWAS summary statistics, eQTL resources, and fine-mapping results with single-cell and spatial data to connect genetic risk to specific cell types, cell states, and genes.
- Translate findings into clear recommendations for discovery and translational program teams, and present them to colleagues and senior management.
- Maintain and extend the team's toolsets, genetics platform, and agentic workflows, evaluating and adopting new methods and tools as the field moves.
- Contribute to academic collaborations, conference presentations, and publications. Institutional support is available for conference travel and publication fees.
Requirements
- Ph.D. in computational biology, bioinformatics, human genetics, or a related field, or equivalent experience.
- Hands-on experience in single-cell or spatial transcriptomics analysis, in statistical genetics (GWAS, eQTL, fine-mapping, colocalization), or in both.
- Proficiency in Python and/or R and familiarity with standard toolkits such as Scanpy, Seurat, PLINK, and LDSC.
- Experience with cloud or high-performance computing environments.
- Ability to explain complex analyses clearly to biologists, program teams, and senior management, in writing and in person.
- Curiosity about disease biology and motivation to see your analyses change the direction of a drug program.
Preferences
- Background in immunology, nephrology, or neuroscience.
- Experience using agentic AI coding tools, or interest in building analysis workflows with them.
- First-author publications or preprints in computational biology or human genetics.
- 0-2 years of previous industry experience.
Travel: Occasional travel to local and national conferences.
Salary range: $135,250 - $168,000
