Senior Scientist - Predictive Modeling & Biomarker Analytics

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.

• 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.

• 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, Tensor Flow).
• 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.