Job Details

Job Description

Target PR Range: 50-60/hr
*Depending on experience

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. 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 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

Equal Opportunity Employer: We are proud to be an equal opportunity employer. We welcome and encourage applications from all qualified candidates regardless of race, sex, gender identity or expression, disability, age, religion or belief, sexual orientation, or any other characteristic protected by applicable laws and regulations. It is our policy not to discriminate against any applicant or employee, and we are committed to fostering a diverse, inclusive, and respectful work environment across all locations in which we operate. We believe that diversity, equity, and inclusion are fundamental to our mission and enhance our ability to serve clients globally. If you have a disability or require any reasonable accommodations during the application or interview process, please inform your recruiter or contact us directly so that we can explore the appropriate arrangements.

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