Data Scientist; SOM Pediatric Neonatology

The School of Medicine Pediatric Neonatology is seeking a Data Scientist to support the management of complex databases as well as develop, maintain, and document code. The Data Scientist will collect and extract data using a variety of data extraction tools and carry out data management, visualization, and analysis tasks.

• Support the development of dashboards, calculations, and reports which may require programming.
• Support the development and tuning of machine learning models, which may require programming.
• Support the development of infrastructure for cleaning and processing data and running experiments to evaluate system performance.
• Mine various data resources for development of key metrics, performance indicators, and general business intelligence useful for planning and service design.
• Perform error analysis and suggest and implement improvements.
• Other duties as assigned.

• Bachelor’s Degree.
• Four years of relevant quantitative research and analytics experience, with at least two of those years including complex programming experience.
• Additional education may substitute for required experience and additional related experience may substitute for required education permitted by the JHU equivalency formula. Beyond a high school diploma/graduation equivalent, to the extent

• Bulk RNA‑seq, single‑cell transcriptomics, spatial transcriptomics, metabolomics, proteomics, and microbiome data
• Proficiency in R and/or Python
• Machine learning (supervised and unsupervised) and deep learning.
• Adept in developmental environments including: RStudio, VS code, Jupyter.
• Experience with tools such as Seurat, Scanpy, DESeq2, edge
  
  R, Monocle, Cell Ranger, QIIME2, HUMAnN, or comparable platforms
• Familiarity with Snakemake, Nextflow, or similar workflow frameworks preferred
• Experience in medical imaging, multi‑omics, and clinical data integration with the ability to deliver reproducible and efficient workflows.
• Proficient in Python, R, SQL, Linux, and deep learning frameworks, with a strong focus on applying computational methods to solve real‑world biomedical problems

• Continuous Improvement - Developing
• Data Architecture - Developing
• Data Management and Analysis - Developing
• Data Tools and Platforms - Developing
• Data Validation and Quality Assurance - Developing
• Data Visualization - Developing
• Machine Learning - Developing
• Oral and written communications - Developing
• Programming Languages - Developing

The core technical skills listed are most essential; additional technical skills may be required based on specific division or department needs.