Company Description At Copoly.ai, we are a dynamic biotech and AI company driving innovation by working on our own proprietary products and developing specialized solutions for our clients. We are transforming the future of early cancer detection through AI-powered diagnostic solutions. Our flagship product, OncoSage, leverages RNA sequencing and proprietary machine learning algorithms to deliver accurate, blood-based cancer detection. We are committed to advancing the field of oncology through cutting-edge technology, improving patient outcomes, and detecting cancer at its earliest stages. Join us in our mission to make revolutionary strides in healthcare technology. Job Overview We are seeking a talented and highly motivated Computational Scientist with a strong analytical background in computational biology and cancer to collaborate with colleagues at one of our clients in the Oncology space. The successful candidate will have a proven track record in multi-omics analysis and the application of both standard and advanced computational methods, such as statistical modeling and bioinformatics pipelines, for hypothesis testing and novel insight generation from patient-derived clinical and biomarker data. 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.