Bioinformatics Scientist

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