Position Title: Bioinformatics Scientist - Cancer Biology & Spatial TranscriptomicsContract Duration: 6 monthsLocation: South San Francisco, CAWork Arrangement: Hybrid or RemoteIntroductionThe 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. AbbVie'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 OverviewThis 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 Requirements1.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 methodologies4.Multi-Omics Analysis: Proficiency in bulk RNA-seq, DNA-seq, and other multi-omics analytical approaches5.Programming Proficiency: Expert-level skills in R and/or Python for data science applications6.Biological Knowledge: Strong understanding of oncogenesis hallmarks, T cell biology, and tumor microenvironment research7.Communication Excellence: Ability to effectively present complex research findings to diverse audiences including computational biologists, non-computational scientists, and senior leadership________________________________________Key ResponsibilitiesData Strategy & Analysis:Develop and execute computational strategies leveraging internal and external bulk, single-cell, and spatial datasets to advance AbbVie's target identification, evaluation, and validation (TIEV) initiativeAnalyze 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 analysesConduct 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/statesUtilize foundation models for single-cell atlas construction, cell type annotation, and in-silico perturbation tasksEmploy integrative spatial and single-cell analysis algorithms/methodsValidation & Translation:Validate identified hypotheses through cross-validation in larger RWD cohorts and comprehensive literature reviewLead computational oncology efforts to provide critical data inputs for advancing AbbVie assets through early development and clinical trial phasesCollaboration & Communication:Effectively communicate and present research progress to diverse cross-functional working groupsFoster collaborative relationships across multi-disciplinary teamsImpact decision-making through clear communication of research findings________________________________________Preferred QualificationsAdvanced Technical Skills:Experience with foundation models and/or deep learning applications in bioinformaticsProficiency in analyzing proteomics and/or functional genomics screening dataExperience with clinical sample multi-omics data for biomarker developmentFamiliarity with NGS data processing tools, statistical analysis, and machine learning frameworksUnderstanding 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 immunogenicityKnowledge 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 improvementCollaborative mindset with ability to work effectively in cross-functional teams Enable Skills-Based Hiring No Provide the days of the week and the hours that this position will require: (No Value) 
 
At SPECTRAFORCE, we are committed to maintaining a workplace that ensures fair compensation and wage transparency in adherence with all applicable state and local laws. This position’s starting pay is: $ 7.25/hr.