Sr. Bioinformatics Scientist

Singular Genomics•San Diego, CA

2d•$131,600 - $154,800•Remote

About The Position

As a Senior Scientist on the Bioinformatics team, you will play a key role in expanding Singular Genomics' spatial biology platform and service offerings. This position sits at the intersection of computational biology, assay development, and hands-on scientific computing. You will report into the Bioinformatics functional group and, initially, be matrixed into the Panel Design team to focus on its most pressing needs. Your initial focus will be supporting the design and optimization of targeted in situ transcriptomics panels for tissue- and disease-specific applications. This includes selecting biologically informative genes for cell type identification, defining pathway-focused content that enables cell-state interpretation, and developing computational tools to automate and improve panel design. As the platform and services continue to evolve, you will contribute to the development of spatial bioinformatics workflows, analysis pipelines, and computational methods that enable customers to generate meaningful biological insight from spatial multiomics data. This is an ideal role for someone who enjoys applying computational approaches to challenging biological problems while building software that directly impacts cutting-edge products and services. This position can be remote or hybrid/on-site. If remote, travel to the headquarters in San Diego, CA will be required at least quarterly.

Requirements

PhD in Bioinformatics, Computational Biology, Genomics, Computer Science, or a related discipline. Candidates with an MS plus industry experience will also be considered.
Experience analyzing spatial transcriptomics, single-cell sequencing, or related genomics datasets.
Strong understanding of tissue biology, cell type annotation, and pathway-based biological interpretation.
Proficiency writing analysis code in Python; able to produce clear, workable code without requiring production-software polish.
Experience building automated analysis workflows or pipelines.
Working knowledge of Linux-based computational environments.
Excellent written and verbal communication skills

Nice To Haves

Experience designing targeted sequencing or hybridization-based panels.
Experience with probe design, oligonucleotide design, or molecular assay development.
Experience with single-cell analysis frameworks such as Scanpy, Squidpy, Seurat, or similar.
Knowledge of cancer biology, immunology, or tissue-specific biology.
Experience developing workflow pipelines using Nextflow.
Familiarity with containerization (e.g., Docker) and reproducible-environment tooling.
Software-engineering rigor (testing, CI/CD, packaging) is welcome but not expected at this level.
Experience with cloud computing environments (AWS preferred).
Familiarity with image-based spatial technologies or microscopy data.
Experience with primary analysis methods such as image processing, feature detection, registration, or sequencing base calling is a plus but not required.

Responsibilities

Support the computational design and optimization of targeted spatial transcriptomics panels for tissue- and disease-specific applications.
Define biologically relevant panel content by integrating current literature, public datasets, and pathway knowledge to support cell type annotation and cell state characterization.
Develop computational methods and software tools for automated panel design, probe evaluation, and panel optimization.
Collaborate closely with molecular biology, chemistry, and assay development teams to iteratively improve probe performance and panel quality.
Evaluate panel performance using internal datasets and external spatial and single-cell reference atlases.
Develop and maintain analysis pipelines for spatial transcriptomics and spatial multiomics datasets.
Apply modern spatial bioinformatics methods to support sequencer / assay development and biological interpretation, including cell typing, differential expression, pathway analysis, spatial neighborhood analysis, and cell-state characterization.
Work with scientists across R&D to develop new analysis capabilities that support expanding service offerings.
Stay current with advances in spatial biology, single-cell genomics, and computational biology, incorporating new approaches where appropriate.
Write clear, workable code (primarily Python) to automate panel design, analysis, and other repetitive scientific tasks.
Build and maintain automated analysis workflows that make routine spatial and single-cell analyses repeatable and easy to run.