• Advanced Degree: PhD in Cancer Biology, Immuno-Oncology, Bioinformatics (with relevant biology focus), or related field (Postdoctoral experience strongly preferred)
• Spatial Transcriptomics Expertise: Hands-on experience with spatial transcriptomics platforms (CosMx experience highly desirable)
• Single-Cell Atlas Development: Proven experience in single-cell atlas creation and batch correction methodologies
• Multi-Omics Analysis: Proficiency in bulk RNA-seq, DNA-seq, and other multi-omics analytical approaches
• Programming Proficiency: Expert-level skills in R and/or Python for data science applications
• Biological Knowledge: Strong understanding of oncogenesis hallmarks, T cell biology, and tumor microenvironment research
• Communication Excellence: Ability to effectively present complex research findings to diverse audiences including computational biologists, non-computational scientists, and senior leadership
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• 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's assets through early development and clinical trial phase
• 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
  

The Company offers the following benefits for this position, subject to applicable eligibility requirements: medical insurance, dental insurance, vision insurance, 401(k) retirement plan, life insurance, long-term disability insurance, short-term disability insurance, paid parking/public transportation, paid time off, paid sick and safe time, hours of paid vacation time, weeks of paid parental leave, and paid holidays annually – as applicable.