Bioinformatics Analyst III

Duration: 6 months Work schedule:

• Hybrid or Remote.

Description: Bioinformatics Scientist - Cancer Biology & Spatial Transcriptomics. 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. 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. Requirements:

• 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.

Key Responsibilities: Data Strategy & Analysis:

• Develop and execute computational strategies leveraging internal and external bulk, single-cell, and spatial datasets to advance 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 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.

Education:

• Advanced Degree: PhD in Cancer Biology, Immuno-Oncology, Bioinformatics (with relevant biology focus), or related field (Postdoctoral experience strongly preferred).

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