Data Scientist - Clinical Machine Learning & Flow Cytometry
About The Position
The Data Scientist, Clinical Machine Learning and Flow Cytometry, will advance diagnostic analytics by developing and implementing machine learning solutions for high-dimensional spectral flow cytometry data. Working with clinical faculty, laboratory scientists, and multidisciplinary data science teams, the role supports improved measurable residual disease (MRD) detection and clinical flow cytometry interpretation through scalable, reproducible, and clinically validated approaches. The work is intended to improve diagnostic accuracy, reduce turnaround times, enhance laboratory efficiency, and support precision diagnostics, translational research, and responsible use of artificial intelligence in healthcare. The position leads the development, validation, and deployment of machine learning solutions for clinical flow cytometry data in collaboration with Clinical Immunopathology faculty and laboratory leadership. It develops analytical frameworks to identify rare and clinically relevant cell populations, improve MRD detection, reduce manual interpretation, and improve diagnostic accuracy and reproducibility. The role also supports machine learning pipelines for spectral flow cytometry, longitudinal quality monitoring systems, and scalable workflows for clinical laboratory operations. Relocation to Memphis is required for candidates who live outside the area; relocation assistance is provided.
Requirements
- Bachelor's degree with at least 10 years of relevant post-degree experience, or master's degree with at least 8 years of relevant experience, or PhD with at least 5 years of relevant experience.
- Relevant experience in areas such as bioinformatics, cheminformatics, statistics or computer science with a background in biological sciences or chemistry.
- Substantial experience in at least one programming or scripting language and at least one statistical package; R is preferred.
- Python is required.
- Ability to develop, validate, and maintain machine learning analyses and pipelines for clinical flow cytometry data.
Nice To Haves
- 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.
- Familiarity with Python libraries including pandas, NumPy, SciPy, Scikit-learn, PyTorch, TensorFlow, XGBoost, and LightGBM.
- R and the Bioconductor ecosystem.
- Experience with flow cytometry data analysis tools and standards, including FCS file formats, FlowCore, FlowJo integration, Cytobank, Spectre, FlowSOM, UMAP, and t-SNE.
- Knowledge of statistical modeling, hypothesis testing, longitudinal analysis, and statistical process control.
- Experience with machine learning model development, validation, deployment, and monitoring.
- Data visualization experience using Plotly, Dash, Streamlit, Shiny, Tableau, or comparable technologies.
- Experience with SQL and NoSQL databases.
- Experience with cloud and high-performance computing environments.
- Experience with Git-based version control and software development best practices.
- Experience with MLOps, reproducible research workflows, and containerization technologies such as Docker and Singularity.
- Familiarity with healthcare data, laboratory information systems, and clinical validation practices.
Responsibilities
- Lead data analysis and formulate machine learning approaches for challenging clinical flow cytometry questions.
- Adapt and optimize analytical methodologies for high-dimensional spectral cytometry and MRD detection.
- Design, develop, validate, and maintain supervised and unsupervised machine learning pipelines for flow cytometry, including clustering, dimensionality reduction, classification, anomaly detection, and predictive modeling.
- Deliver data products, analytical reports, visualizations, and technical documentation supporting 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 in clinical flow cytometry and laboratory quality monitoring.
- Develop longitudinal assay-performance monitoring methods, including statistical process control, drift detection, and assessment of instrument, reagent, and workflow variability.
- Recommend and implement workflow automation and improvements that increase throughput, reproducibility, and operational efficiency.
- Evaluate, benchmark, and test emerging machine learning methods, algorithms, and technologies for biomedical and clinical diagnostics; develop reusable code, workflows, and software tools.
- 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.
- Lead and participate in interdisciplinary projects spanning data science, laboratory medicine, clinical diagnostics, and translational research; act as project manager when required.
Benefits
- Relocation assistance
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What This Job Offers
Job Type
Full-time
Career Level
Senior
