Bioinformatics Research Scientist II
Job Description:
ā¢ 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.
ā¢ 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.
ā¢ Document methods, analyses, and results to support reproducibility and regulatory-grade research standards.
ā¢ 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.
ā¢ Collaborate closely with multidisciplinary teams to support research initiatives that inform product development and scientific strategy.
Requirements:
ā¢ PhD. in Cancer Biology, Bioinformatics, Statistics or related field, or M.Sc. with 3-4 years of relevant post-graduate experience (postdoc or industry).
ā¢ Deep familiarity with cancer genomics, pathology, or clinical management (prostate cancer preferred).
ā¢ Hands-on experience analyzing transcriptomics and NGS data.
ā¢ Expertise in R programming and data analysis.
ā¢ Strong proficiency in feature reduction techniques and visualization (e.g., U-MAP), supervised and unsupervised learning algorithms, and model performance evaluation.
ā¢ Hands-on experience with cloud computing architecture (AWS preferred).
ā¢ Strong problem-solving skills and intellectual curiosity, with a desire to learn new disease biology and clinical concepts.
ā¢ Excellent communication skills, with experience presenting or discussing scientific results in collaborative settings.
Benefits:
ā¢ Competitive compensation
ā¢ Significant career opportunities
ā¢ Inclusive workforce
ā¢ 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.
ā¢ 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.
ā¢ Document methods, analyses, and results to support reproducibility and regulatory-grade research standards.
ā¢ 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.
ā¢ Collaborate closely with multidisciplinary teams to support research initiatives that inform product development and scientific strategy.
Requirements:
ā¢ PhD. in Cancer Biology, Bioinformatics, Statistics or related field, or M.Sc. with 3-4 years of relevant post-graduate experience (postdoc or industry).
ā¢ Deep familiarity with cancer genomics, pathology, or clinical management (prostate cancer preferred).
ā¢ Hands-on experience analyzing transcriptomics and NGS data.
ā¢ Expertise in R programming and data analysis.
ā¢ Strong proficiency in feature reduction techniques and visualization (e.g., U-MAP), supervised and unsupervised learning algorithms, and model performance evaluation.
ā¢ Hands-on experience with cloud computing architecture (AWS preferred).
ā¢ Strong problem-solving skills and intellectual curiosity, with a desire to learn new disease biology and clinical concepts.
ā¢ Excellent communication skills, with experience presenting or discussing scientific results in collaborative settings.
Benefits:
ā¢ Competitive compensation
ā¢ Significant career opportunities
ā¢ Inclusive workforce