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Senior Scientist - Predictive Modeling & Biomarker Analytics

Q Bio · Redwood City, United States Of America · On-site

$175,000.00  -  $215,000.00

  • Office in Redwood City
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At Q Bio, we are transforming healthcare by combining AI, Physics, and Biology to automate the physical exam, making preventive, personalized care accessible to all. We are hiring a Senior Scientist focused on predictive modeling and biomarker analytics.

The Role:
We are looking for a hands-on senior applied scientist or engineer with experience in predictive modeling, biomarker stratification, and multi-modal data integration. You will be responsible for building models that uncover patterns, correlations, and risk signatures across imaging, bloodwork, and genomics — turning Q Bio’s deep datasets into actionable health insights. This role is highly technical and execution-focused: you will design, prototype, validate, and help produce predictive models in collaboration with our engineering, product, and clinical teams.

What You Will Do:
  • Develop and deploy predictive models and risk stratification frameworks linking imaging, lab, and genomic biomarkers.
  • Implement pattern recognition and feature correlation pipelines to detect early biological changes across systems (e.g., neuro–metabolic, musculoskeletal–metabolic).
  • Translate scientific hypotheses into computational models that can be tested and validated using Q Bio’s internal datasets.
  • Integrate models into the Gemini and Constellation platforms for visualization and clinical interpretation.
  • Collaborate with engineers on scalable, production-ready codebases and model deployment pipelines.
  • Leverage Q Bio’s diverse datasets and external cohorts for model validation.
  • Partner with clinical and regulatory stakeholders to ensure model robustness and alignment with submission requirements.
What You Will Bring:
  • MS or PhD in Biomedical Engineering, Computational Biology, Data Science, or related quantitative discipline.
  • 6+ years of experience building and validating machine learning or predictive models in biomedical or imaging domains.
  • Advanced proficiency in Python, scientific computing, and ML frameworks (scikit-learn, PyTorch, TensorFlow).
  • Deep understanding of statistical learning, data normalization, and model interpretability in heterogeneous biomedical datasets.
  • Track record of delivering production-quality analytical models or pipelines (not just prototypes).
  • Excellent communication skills and the ability to collaborate with cross-functional teams (engineering, clinical, product).
    • Preferred
  • Experience integrating quantitative imaging data (MRI, qMRI, CT) with biochemical, genomic, or clinical biomarkers.
  • Familiarity with biological pathway modeling or multi-omics integration.
  • Exposure to regulated environments (FDA submissions, IRB-approved studies, clinical validation).
  • Demonstrated ability to move from concept to deployment in small, fast-paced teams