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.

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.

Bioinformatics Research Scientist II

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