New 
Bioinformatics Scientist
 Spectraforce Technologies | |
 United States, California, San Francisco | |
 Mar 17, 2026 | |
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Job Title: Position Title: Bioinformatics Scientist - Cancer Biology & Spatial Transcriptomics
Contract Duration: 6 months Location: South San Francisco, CA Work Arrangement: Hybrid or Remote Introduction The Quantitative Medicine & Genomics (QM&G), (Genomic Research Center, Computational Oncology, Research and Early Development group (GRC-CORED) is seeking a highly motivated computational biologist to play an integral role in a multi-disciplinary team focused on developing new therapies and approaches for cancer treatment. Client's GRC is a center of excellence for bioinformatics, functional genomics, human genetics, and pharmacogenomics, working across all R&D including discovery, clinical development, process sciences, global epidemiology, and corporate strategy. Role Overview This is an exceptional opportunity to advance the Immuno-Oncology pipeline through discovery-focused research while supporting existing programs. You will characterize immune microenvironments of solid tumors to better understand anti-tumor immune responses, utilizing cutting-edge genomics platforms including spatial/single-cell transcriptomics, proteomics, and advanced analytical algorithms. Your expertise will directly influence data-driven drug discovery and impact patients' lives. This role offers opportunities to publish findings with excellent work/life balance. Essential Requirements 1. Advanced Degree: PhD in Cancer Biology, Immuno-Oncology, Bioinformatics (with relevant biology focus), or related field (Postdoctoral experience strongly preferred) 2. Spatial Transcriptomics Expertise: Hands-on experience with spatial transcriptomics platforms (CosMx experience highly desirable) 3. Single-Cell Atlas Development: Proven experience in single-cell atlas creation and batch correction methodologies 4. Multi-Omics Analysis: Proficiency in bulk RNA-seq, DNA-seq, and other multi-omics analytical approaches 5. Programming Proficiency: Expert-level skills in R and/or Python for data science applications 6. Biological Knowledge: Strong understanding of oncogenesis hallmarks, T cell biology, and tumor microenvironment research 7. Communication Excellence: Ability to effectively present complex research findings to diverse audiences including computational biologists, non-computational scientists, and senior leadership Key Responsibilities Data Strategy & Analysis: * Develop and execute computational strategies leveraging internal and external bulk, single-cell, and spatial datasets to advance client's target identification, evaluation, and validation (TIEV) initiative * Analyze spatial transcriptomics data from patient clinical trials to dissect tumor microenvironment mechanisms of action (MOA) * Consolidate pre-clinical and real-world data (RWD) sets to create population cohorts for downstream analyses * Conduct bulk RNAseq and DNAseq analysis & other omics data analysis from clinical patients' samples to discover novel targets, biological pathways and predictive biomarker for clinical response. Computational Innovation: * Apply machine learning and deep learning approaches to link high-dimensional genomics features to oncogenic and immunosuppressive cellular programs/states * Utilize foundation models for single-cell atlas construction, cell type annotation, and in-silico perturbation tasks * Employ integrative spatial and single-cell analysis algorithms/methods Validation & Translation: * Validate identified hypotheses through cross-validation in larger RWD cohorts and comprehensive literature review * Lead computational oncology efforts to provide critical data inputs for advancing client assets through early development and clinical trial phases Collaboration & Communication: * Effectively communicate and present research progress to diverse cross-functional working groups * Foster collaborative relationships across multi-disciplinary teams * Impact decision-making through clear communication of research findings Preferred Qualifications Advanced Technical Skills: * Experience with foundation models and/or deep learning applications in bioinformatics * Proficiency in analyzing proteomics and/or functional genomics screening data * Experience with clinical sample multi-omics data for biomarker development * Familiarity with NGS data processing tools, statistical analysis, and machine learning frameworks * Understanding of container technologies for pipeline deployment (Docker, AWS Container, etc.) * Knowledge of assay technologies and algorithm principles (WES/WGS, Mass-spec proteomics, ATAC-seq, etc.) Biological Expertise: * Deep understanding of cellular signaling, metabolism, and/or tumor immunogenicity * Knowledge of tumor-intrinsic and/or T-cell biology (metabolic, mitogenic, fibrotic, and innate immune pathways; T cell exhaustion) Soft Skills: * Creates a learning environment that is open to suggestions and experimentation for continuous improvement * Collaborative mindset with ability to work effectively in cross-functional teams | |