Job Description:

• Analyze gene expression, transcriptomic, and next-generation sequencing data applying state-of-the-art statistical and machine-learning methods to derive biological and clinical insights, with a primary focus on prostate cancer cohorts from clinical and research studies.
• Design, train, and evaluate supervised and unsupervised machine learning models to predict disease subtypes, biological and clinical endpoints, and clinically actionable genomic signatures across multiple disease areas.
• Document methods, analyses, and results to support reproducibility and regulatory-grade research standards.
• Translate findings into presentations, abstracts, and publication to be presented to internal teams as well as external collaborators, including academic researchers, clinicians, and commercial partners.
• Collaborate closely with multidisciplinary teams to support research initiatives that inform product development and scientific strategy.

Requirements:

• PhD. in Cancer Biology, Bioinformatics, Statistics or related field, or M.Sc. with 3-4 years of relevant post-graduate experience (postdoc or industry).
• Deep familiarity with cancer genomics, pathology, or clinical management (prostate cancer preferred).
• Hands-on experience analyzing transcriptomics and NGS data.
• Expertise in R programming and data analysis.
• Strong proficiency in feature reduction techniques and visualization (e.g., U-MAP), supervised and unsupervised learning algorithms, and model performance evaluation.
• Hands-on experience with cloud computing architecture (AWS preferred).
• Strong problem-solving skills and intellectual curiosity, with a desire to learn new disease biology and clinical concepts.
• Excellent communication skills, with experience presenting or discussing scientific results in collaborative settings.

Benefits:

• Competitive compensation
• Significant career opportunities
• Inclusive workforce