Data Scientist - Clinical Machine Learning & Flow Cytometry
Core
Develop and deploy machine learning solutions for high-dimensional spectral flow cytometry data to improve measurable residual disease (MRD) detection and clinical flow cytometry interpretation.
Role type
Senior IC machine learning engineer (clinical diagnostics)
Builds
Production ML pipelines for automated identification of rare cell populations and longitudinal quality monitoring systems
Domain
Healthcare + Biomedical Data Science (Flow Cytometry)
Deliverable
production ML models
Required skills
Python (pandas, NumPy, Scikit-learn, PyTorch, TensorFlow, XGBoost, LightGBM), R, Bioconductor, Flow cytometry data analysis (FCS, FlowCore, FlowJo, Cytobank, Spectre, FlowSOM, UMAP, t-SNE), Statistical modeling, Machine learning model development, Data visualization (Plotly, Dash, Streamlit, Shiny, Tableau), SQL/NoSQL, Git
Preferred skills
Cloud/High-performance computing, Feature engineering, Explainable AI, Statistical process control, Longitudinal analysis
Technologies
PyTorch, TensorFlow, XGBoost, LightGBM, FlowJo, Cytobank, Spectre, FlowSOM, UMAP, t-SNE, Plotly, Dash, Streamlit, Shiny, Tableau, FCS
Responsibilities
Design and implement analytical frameworks for automated identification of rare cell populations; Develop and validate ML pipelines for supervised/unsupervised analysis including clustering and dimensionality reduction; Establish protocols for reproducible workflows and longitudinal monitoring of assay performance; Collaborate with pathologists and lab scientists to translate clinical questions into computational solutions; Mentor junior analysts and contribute to scientific publications.
Seniority
Senior, hands-on IC
