Senior Scientist - Predictive Modeling & Biomarker Analytics presso Q Bio
Q Bio · Redwood City, Stati Uniti d'America · On-site
- Ufficio in Redwood City
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).
- 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
Preferred
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