Data Scientist I

University of Florida•Gainesville, FL

3d•$61,000 - $65,000

About The Position

Responsible for designing, developing, maintaining, and supporting software applications, computational workflows, and data systems for biomedical and clinical research. The position applies principles of computer science, software engineering, data engineering, and computational methods to support the PLuS2 project and future research initiatives. The role includes developing research software and data-processing pipelines, integrating heterogeneous research datasets, automating analytical workflows, implementing computational and statistical methods, creating data visualizations, conducting quality assurance activities, and developing reproducible research tools. The Data Scientist I collaborates with investigators, biostatisticians, clinicians, and research staff to translate research needs into reliable, scalable, and efficient computational solutions that support research operations and scientific discovery.

Requirements

Bachelor’s degree in computer or physical science, statistics, bioinformatics, analytics, or a similar field and two years of experience; or a master’s degree in computer or physical science, statistics, bioinformatics, analytics, or a similar field.

Nice To Haves

Master’s degree in computer science, computer engineering, data science, bioinformatics, computational science, or a related field.
Experience developing software or computational tools for biomedical, clinical, scientific, or research applications.
Proficiency in one or more programming languages such as Python, R, Java, C++, or JavaScript/TypeScript.
Experience with software engineering principles, Git/version control, testing, debugging, APIs, databases, and software documentation.
Experience designing and implementing data-processing or ETL pipelines and working with large or heterogeneous datasets.
Experience with relational databases and SQL, including database design, querying, and data integration.
Experience with machine learning, statistical computing, data visualization, or other computational research methods.
Experience developing reproducible research workflows and automated computational pipelines.
Experience working in Linux, cloud computing, high-performance computing, containers, or research computing environments.
Experience collaborating with researchers, biostatisticians, clinicians, or interdisciplinary research teams.

Responsibilities

Design, develop, test, maintain, and document research software applications, scripts, APIs, and computational tools for biomedical and clinical research projects, including the PLuS2 project and related initiatives.
Ensure software solutions are reliable, scalable, secure, and well documented.
Develop, implement, and maintain automated, reproducible data-processing pipelines to integrate, transform, clean, validate, and manage clinical, electronic health record, registry, survey, imaging, and other research datasets.
Ensure data quality, integrity, security, and reproducibility in accordance with research and institutional standards.
Develop and apply natural language processing, machine learning, and artificial intelligence methods to research data, including extracting and analyzing information from clinical notes, medical records, survey responses, and other unstructured sources.
Create computational models, AI-based applications, and text-processing pipelines to support research objectives and improve research efficiency.
Collaborate with investigators, biostatisticians, and research staff to implement computational and statistical methods that address research questions and study objectives.
Develop analytical workflows, predictive models, data visualizations, and other computational solutions to support data analysis, interpretation of findings, and dissemination of results.
Develop, maintain, and enhance automated research workflows and computational infrastructure to improve the efficiency, scalability, and reproducibility of research activities.
Integrate databases, APIs, cloud-based resources, and research systems as needed to support ongoing and future projects.