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Bioinformatics Research Scientist II

Remote, USA Full-time Posted 2026-06-29

Job Description:

  • Analyze gene expression, transcriptomic, and reputed company reputed company 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 reputed company 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

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