Scientist, Computational Biology

Visterra•Waltham, MA

6d•$135,250 - $168,000•Onsite

About The Position

Visterra is seeking a Scientist to join its Genomics Platform team, which supports target identification, indication prioritization, and the progression of drug candidates in clinical development. The team uses single-cell multi-omics, spatial transcriptomics, and human genetics to characterize human disease, particularly in kidney, autoimmune, and neurodegenerative indications. The Scientist will own projects end to end, from initial quality control through biological interpretation and presentation of insights. The role includes framing questions with program teams, shaping experimental designs for omics data generation, analyzing internally generated and public human datasets, and presenting findings to stakeholders, collaborating teams, and senior management. The six-person Genomics Platform team includes computational and experimental researchers who design experiments and analyze results together. The team focuses primarily on human data, generating paired single-cell and spatial transcriptomics data from human tissue and combining it with public disease atlases and population genetics resources. It participates in the FNIH Accelerating Medicines Partnership in Autoimmune and Immune-Mediated Diseases (AMP AIM) and has an internal genetics platform that prioritizes target genes using GWAS summary statistics, fine-mapping, colocalization, gene-level association, and single-cell integration. The team works in AWS, uses Claude Code for analyses, and is developing agentic workflows spanning data acquisition, analysis, and target reasoning. The Scientist will contribute to academic collaborations and aims to publish. This is a full-time position located at Visterra’s offices in Waltham, Massachusetts, with occasional travel to local and national conferences.

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, statistical genetics (GWAS, eQTL, fine-mapping, colocalization), or 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 analyses change the direction of a drug program.

Nice To Haves

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.

Responsibilities

Own projects from question to recommendation, including 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 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 evolves.
Contribute to academic collaborations, conference presentations, and publications.
Analyze human disease tissue, including AMP AIM datasets across autoimmune diseases, to identify pathogenic cell states and potential target molecules.
Integrate genetic, single-cell, and spatial evidence across diseases to prioritize indications for pipeline targets.
Integrate genetic evidence across diseases and pathways and explore immune mechanisms in neurodegenerative disease as a source of new targets.