Senior Scientist - Predictive Modeling & Biomarker Analytics

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