Key Responsibilities

• Collaborate with scientists in computational oncology and translational medicine to analyze clinical datasets and address scientific hypotheses.
• Develop analytical approaches to integrate and interpret data, delivering insights into disease biology and mechanisms of action to propel pipeline and clinical research goals.
• Synthesize complex computational results into clear, actionable insights for internal medical stakeholders and external scientific publications.
• Thoroughly document code, analysis processes, and findings.

Qualifications & Requirements

• Education:  Master's or PhD in Computational Biology, Bioinformatics, or a related field.
• Experience:  3-4 years of experience in the analysis of large-scale bulk data (RNA, WES), single-cell RNA-seq, ctDNA, clinical, and real-world data.
• Analytical Expertise:  Proficiency in gold-standard methods and advanced techniques, including multivariate statistical analyses, multimodal data integration methods, and predictive/prognostic analyses (e.g., Kaplan-Meier survival analysis).
• Domain Knowledge:  Expertise in cancer biology and clinical omics data is required; experience with real-world data is a bonus.
• Technical Skills:  Proficiency in R and shell scripting is required; knowledge of Python is a bonus.
• Tools:  Experience working with Git and high-performance computing.
• Soft Skills:  High self-motivation with the ability to produce high-quality analysis results with minimal supervision, meet key deadlines, and make independent decisions. Excellent communication skills and experience working as part of a team are essential.