Data Scientist I

University of Florida•Gainesville, FL

17h•$65,000 - $85,000

About The Position

Data Acquisition, Integration & Management 􀁸 Acquire, link, and manage large-scale datasets (e.g., Medicaid T-MSIS, commercial claims, EHR/dental EHR) 􀁸 Develop reproducible pipelines for data ingestion, cleaning, and transformation 􀁸 Ensure high data quality through validation, auditing, and documentation 􀁸 Implement scalable workflows for handling longitudinal and multi-source data Advanced Statistical Analysis & Modeling 􀁸 Design and implement statistical and quasi-experimental analyses (e.g., regression, survival analysis, causal inference, difference-in-differences) 􀁸 Apply machine learning methods (e.g., classification, clustering, predictive modeling) where appropriate 􀁸 Evaluate model performance and ensure interpretability and robustness 􀁸 Collaborate with investigators to translate research questions into rigorous analytic plans Programming, Automation & Reproducible Research 􀁸 Write efficient, well-documented code in R, Python, SAS, or SQL 􀁸 Build reproducible workflows using version control (e.g., Git) and structured pipelines 􀁸 Automate repetitive tasks and optimize data processing performance 􀁸 Maintain clear documentation for all analytic processes to ensure transparency and reproducibility AI Integration & Responsible Use 􀁸 Leverage AI tools (e.g., NLP, large language models, automation tools) to enhance productivity and insight generation 􀁸 Apply AI responsibly with attention to bias, validity, privacy, and reproducibility 􀁸 Document AI-assisted workflows and ensure all outputs meet scientific rigor standards 􀁸 Stay current with emerging AI applications in healthcare data science Data Visualization & Communication 􀁸 Develop clear, high-quality visualizations and dashboards for diverse audiences 􀁸 Translate complex analyses into actionable insights for researchers, clinicians, and policymakers 􀁸 Prepare figures, tables, and analytic summaries for manuscripts, grants, and presentations Collaboration with Faculty & Research Teams 􀁸 Work closely with investigators to design studies, define variables, and interpret findings 􀁸 Provide technical expertise in study design, data structure, and analytic methods 􀁸 Support interdisciplinary collaboration across clinical, policy, and informatics teams Data Governance, Security & Compliance 􀁸 Ensure compliance with HIPAA, DUAs, and institutional data governance policies 􀁸 Maintain secure data environments and protect sensitive information 􀁸 Document data provenance, transformations, and analytic decisions

Requirements

A Bachelor's Degree in data science, statistics, bioinformatics, analytics, or similar field and two years of experience
Master's Degree in data science, statistics, bioinformatics, analytics, or similar field.

Nice To Haves

Master’s degree in biostatistics, data science, epidemiology, computer science, health informatics, or related field or Bachelor’s degree with 3+ years of relevant experience
Demonstrated experience working with Medicaid claims, commercial claims, and/or EHR data
Strong programming skills in R and/or Python (SAS/SQL desirable)
Experience with causal inference, longitudinal data analysis, or survival analysis
Familiarity with health services research, policy analysis, or population health
Experience applying machine learning and/or AI methods in healthcare data
Strong track record of clean, well-documented, reproducible code and workflows

Responsibilities

Acquire, link, and manage large-scale datasets (e.g., Medicaid T-MSIS, commercial claims, EHR/dental EHR)
Develop reproducible pipelines for data ingestion, cleaning, and transformation
Ensure high data quality through validation, auditing, and documentation
Implement scalable workflows for handling longitudinal and multi-source data
Design and implement statistical and quasi-experimental analyses (e.g., regression, survival analysis, causal inference, difference-in-differences)
Apply machine learning methods (e.g., classification, clustering, predictive modeling) where appropriate
Evaluate model performance and ensure interpretability and robustness
Collaborate with investigators to translate research questions into rigorous analytic plans
Write efficient, well-documented code in R, Python, SAS, or SQL
Build reproducible workflows using version control (e.g., Git) and structured pipelines
Automate repetitive tasks and optimize data processing performance
Maintain clear documentation for all analytic processes to ensure transparency and reproducibility
Leverage AI tools (e.g., NLP, large language models, automation tools) to enhance productivity and insight generation
Apply AI responsibly with attention to bias, validity, privacy, and reproducibility
Document AI-assisted workflows and ensure all outputs meet scientific rigor standards
Stay current with emerging AI applications in healthcare data science
Develop clear, high-quality visualizations and dashboards for diverse audiences
Translate complex analyses into actionable insights for researchers, clinicians, and policymakers
Prepare figures, tables, and analytic summaries for manuscripts, grants, and presentations
Work closely with investigators to design studies, define variables, and interpret findings
Provide technical expertise in study design, data structure, and analytic methods
Support interdisciplinary collaboration across clinical, policy, and informatics teams
Ensure compliance with HIPAA, DUAs, and institutional data governance policies
Maintain secure data environments and protect sensitive information
Document data provenance, transformations, and analytic decisions