Data Scientist - Clinical Machine Learning & Flow Cytometry

Job Responsibilities

• Lead data analysis and deliver high-quality results by formulating advanced and innovative machine learning approaches to address challenging clinical flow cytometry analysis questions.
• Adapt and optimize analytical methodologies to support high-dimensional spectral cytometry and MRD detection initiatives.
• Design, develop, validate, and maintain machine learning pipelines for supervised and unsupervised analysis of flow cytometry data, including clustering, dimensionality reduction, classification, anomaly detection, and predictive modeling.
• Deliver data products, analytical reports, visualizations, and technical documentation that support clinical implementation, regulatory review, and scientific publication.
• Document analytical methods, model performance, validation results, and quality assurance procedures.
• Establish and document protocols, best practices, and reproducible workflows for machine learning applications in clinical flow cytometry and laboratory quality monitoring.
• Develop methods for longitudinal monitoring of assay performance, including statistical process control, drift detection, and quality assessment of instrument, reagent, and workflow variability.
• Recommend opportunities to automate and improve existing analytical workflows and implement enhancements that increase throughput, reproducibility, and operational efficiency.
• Evaluate, benchmark, and test emerging machine learning methods, algorithms, and technologies applicable to biomedical and clinical diagnostics.
• Develop reusable code, workflows, and software tools that can be leveraged across projects and laboratories.
• Collaborate with pathologists, laboratory scientists, bioinformaticians, statisticians, and data scientists to translate clinical and scientific questions into computational solutions.
• Participate in manuscript preparation, scientific presentations, abstracts, and dissemination of project outcomes to internal and external research communities.
• Lead and participate in interdisciplinary projects involving data science, laboratory medicine, clinical diagnostics, and translational research.
• Act as project manager when required.

Preferred Technical Skills

• Python (required), including pandas, NumPy, SciPy, Scikit-learn, PyTorch, TensorFlow, XGBoost, and LightGBM.
• R and Bioconductor ecosystem.
• Flow cytometry data analysis tools and standards, including FCS file formats, FlowCore, FlowJo integration, Cytobank, Spectre, FlowSOM, UMAP, and t-SNE.
• Statistical modeling, hypothesis testing, longitudinal analysis, and statistical process control.
• Machine learning model development, validation, deployment, and monitoring.
• Data visualization using Plotly, Dash, Streamlit, Shiny, Tableau, or comparable technologies.
• SQL and NoSQL databases.
• Cloud and high-performance computing environments.
• Git-based version control and software development best practices.
• Experience with MLOps, reproducible research workflows, and containerization technologies, including Docker and Singularity.
• Familiarity with healthcare data, laboratory information systems, and clinical validation practices.

Minimum Requirements

Bachelor's degree with 10+ years of relevant post-degree work experience in relevant area (e.g., bioinformatics, cheminformatics, statistics/computer science with a background in biological sciences or chemistry) OR Master's degree with 8+ years of relevant experience OR PhD with 5+ years of relevant experience. Substantial experience in at least one programming or scripting language and at least one statistical package, with R preferred.

Preferred Qualifications

• Experience applying machine learning to biomedical, clinical, translational, or laboratory datasets.
• Experience with high-dimensional single-cell or flow cytometry data.
• Experience developing and validating analytical methods in regulated or clinical laboratory environments.
• Demonstrated record of scientific publication and interdisciplinary collaboration.
• Experience translating research algorithms into operational workflows that improve efficiency, quality, or patient care.