data scientist
Role details
Job location
Tech stack
Job description
The Department of Epidemiology and Cancer Control at St. Jude Children's Research Hospital is recruiting an innovative data scientist to join our multidisciplinary team advancing research in childhood cancer survivorship as a Faculty Member at the FULL, ASSOCIATE, or ASSISTANT level. We are looking for a scholar to apply state-of-the-art computational methods to extract insights from high-dimensional, multimodal health data and develop predictive and integrative models that transform survivorship care.
Our Department's mission is to conduct cutting-edge clinical, biological, and population-health research, and translate findings into effective strategies to avert or mitigate cancer-treatment-related complications and improve the quality of life of childhood cancer survivors. We are seeking a creative and collaborative data scientist to lead a vibrant research program at the intersection of data science and clinical/biological sciences.
We actively collaborate across Cancer Center Programs, engaging with both clinic- and laboratory-based researchers to advance clinical care and knowledge of childhood cancer and associated late effects. Our research leverages diverse and complex data streams, including:
- Time-series and cross-sectional sensor data from wearable devices
- Image and video-based phenotyping for functional assessments
- Multi-omics (whole genome/whole exome sequencing, RNA-seq, methylation, proteomics/metabolomics, CHIP) and biomarker profiles from biological samples
- Physiological signals such as EKG
- Longitudinal clinical, functional, and behavioral assessments
- Usability evaluation
- Geographical and spatiotemporal data on environmental and socioeconomic context
Requirements
- Advanced machine learning (deep learning, representation learning, probabilistic modeling)
- Natural language processing and large language models for extracting, structuring, and modeling high-dimensional, unstructured clinical notes, patient-reported narratives, and speech-derived phenotypes
- AI-driven approaches for multimodal data integration and predictive analytics
- Computational pipelines for large-scale biomedical datasets
- Interest in causal inference, risk stratification, and precision medicine
- Experience in transforming complex clinical/biological data into meaningful representations (including advanced feature engineering)
- Experience applying pretrained foundation models or self-supervised learning approaches to biomedical data
- AI agents for orchestrating multi-stream data integration, real-time analytics, and decision-support workflows in survivorship research, Successful applicants must hold a PhD or equivalent degree in the field of data science, have at least three years of relevant postgraduate experience, and have a proven track record of productivity.