Senior Scientist, Computational Biology

Xaira Therapeutics•South San Francisco, CA

4h•$136,000 - $170,000•Onsite

About The Position

Xaira is seeking a highly motivated and experienced Senior Scientist to join our Computational Biology team with a focus on supporting large-scale single-cell perturbation screens. The ideal candidate will bring deep expertise in single-cell RNA sequencing, Perturb-seq, and a proven track record of processing and integrating large-scale datasets from multiple experimental sources. This position offers the opportunity to contribute to cutting-edge efforts at the intersection of functional genomics, machine learning, and disease biology. You will work closely with wet-lab scientists, AI/ML scientists, automation engineers, and disease biology teams to develop scalable perturbation workflows, implement new technologies, and drive high-quality data generation and experimental innovation across multiple therapeutic areas. This will be an on-site role in South San Francisco.

Requirements

Ph.D. in Computational Biology, Bioinformatics, Genomics, or a related quantitative field, with 5+ years of relevant industry and/or applied research experience (candidates with fewer years are also encouraged to apply and job level can be adjusted as appropriate)
Deep expertise in single-cell RNA sequencing analysis with substantial hands-on experience in Perturb-seq or CRISPR-based perturbation screen data, including thorough familiarity with the full processing stack from raw reads to interpretable outputs
Demonstrated ability to integrate heterogeneous datasets from multiple sources, with a strong command of batch correction, normalization, and quality control strategies tailored to large-scale perturbation experiments
Experience building and maintaining production bioinformatics workflows (e.g. Nextflow), comfort with containerized environments (Docker) and cloud computing platforms (AWS)
Strong software engineering fundamentals: proficiency in Python (numpy, pandas, scanpy, scikit-learn), fluency in Unix environments, and experience with version control and collaborative software development via GitHub.
Working knowledge of statistical and machine learning methods applied to biological data, with the ability to critically evaluate analytical choices and collaborate effectively with AI/ML teammates
Demonstrated ability to independently lead scientific initiatives, drive cross-functional collaboration, and communicate quantitative results to both wet lab and computational audiences
Excellent analytical, organizational, written, and verbal communication skills, with the ability to thrive in fast-paced, highly collaborative environments focused on scientific innovation

Responsibilities

Own end-to-end computational analysis of Perturb-seq datasets, spanning quality control, biological interpretation, and translation of large-scale single-cell data into actionable insights for therapeutic discovery and AI/ML modeling
Design and implement standardized analytical frameworks for integrating Perturb-seq data across diverse experimental contexts, such as cell types, library designs, and sequencing platforms, to enable reliable cross-source analysis
Build and maintain scalable, production-grade bioinformatics pipelines, with an emphasis on reproducibility, modularity, and performance at the scale of large perturbation screens
Integrate Perturb-seq data with complementary omics modalities, including CITE-seq, ATAC-seq, spatial transcriptomics, as well as external reference datasets, to enrich biological context and support multi-modal analyses
Partner closely with AI/ML scientists to ensure data products meet modeling requirements, and collaborate with wet-lab scientists to translate experimental design into optimal computational workflows
Manage code repositories, maintain versioned and documented workflows, and uphold computational environment standards that ensure long-term pipeline reliability
Contribute to scientific strategy, publications, and patents while mentoring and providing scientific guidance to researchers within the organization