| Job Description: |
The Data Scientist IV serves as a senior decision scientist within the UF College of Medicine’s Quality and Patient Safety initiative (QPSi), providing expert-level leadership in the design and execution of advanced analytic strategies that inform the safe and effective deployment of AI-enabled interventions in clinical workflows.
The incumbent brings deep proficiency in statistical theory, causal inference, and probabilistic modeling, and works with interdisciplinary clinical, operational, and informatics teams to develop rigorous statistical analysis plans and decision-analytic frameworks for complex clinical questions relevant to quality improvement and patient safety.
This role serves as a critical bridge between AI technology investments and measurable clinical impact, ensuring that predictive models and AI-enabled tools are deployed safely, monitored rigorously, and evaluated for their effect on patient outcomes — directly advancing QPSi’s mission to eliminate preventable harm by integrating innovative AI tools with expert care.
About This Role:
- Leads the development of decision-science and statistical strategies that inform the safe and effective deployment of AI-enabled interventions in clinical workflows. Provides advanced consultation to interdisciplinary clinical, operational, and informatics teams to define decision questions, endpoints, and evaluation approaches. Develops and reviews statistical analysis plans for intervention evaluation, including the application of causal inference methods to estimate impact on clinician behavior, care processes, and patient outcomes under real-world conditions. Produces and validates analytic outputs (tables, figures, and decision summaries) that communicate results and uncertainty to stakeholders and leadership. Analytic outputs directly inform health system leadership decisions on AI deployment scope, expansion, and discontinuation.
- Designs and executes simulation-based evaluations to anticipate workflow effects and operational consequences of AI interventions prior to and after implementation. Applies Bayesian statistical modeling and probabilistic reasoning to quantify uncertainty and evaluate candidate intervention strategies in light of clinical and operational tradeoffs (e.g., benefit, burden, timeliness, and resource constraints). Where appropriate, evaluates sequential decision strategies - including reinforcement-learning-informed policy comparisons - in a controlled and reviewable manner to support decisions such as alert thresholds, routing, timing, and escalation pathways. Summarizes scenario-testing results into clear recommendations for implementation teams and governance stakeholders.
- Creates and validates analysis datasets and/or reviews those developed by others to ensure data quality, appropriate methods, and accurate calculation of variables from heterogeneous clinical data sources. Develops well-documented, reproducible code and technical documentation to support meetings, presentations, and manuscripts. Contributes to AI governance activities by supporting pre-deployment analytic review, defining monitoring metrics, and performing postimplementation impact evaluation, including periodic performance assessment and interpretability/quality checks as required by program needs.
About COM's Quality and Patient Safety Initiative (QPSi):
The UF College of Medicine's Quality and Patient Safety Initiative (QPSi) is dedicated to enhancing patient care quality and safety through innovative research, education, and collaboration. This initiative focuses on implementing evidence-based practices, advancing patient safety initiatives, and fostering a culture of continuous improvement within the College of Medicine.
QPSi's mission is to drive excellence in healthcare delivery by integrating clinical expertise, data-driven insights, and cutting-edge technology. The initiative engages healthcare professionals, students, and stakeholders in efforts to elevate patient care standards and outcomes.
For more information about the Quality and Patient Safety Initiative, visit UF College of Medicine's QPSi.
We Offer Exceptional Benefits:
- Low-cost State Health Plans: Medical, Dental, and Vision Insurance
- Life and Disability Insurance
- Generous Retirement Options to secure your future
- Comprehensive Paid Time Off Packages: (includes over 10 paid holidays, as well as paid family, sick and vacation leave)
- Exceptional Personal and Professional Development Opportunities: Access to UF Training & Organizational Development programs, leadership development, LinkedIn Learning, and more
- Tuition Assistance through the UF Employee Education Program
- Public Service Loan Forgiveness (PSLF) Eligible Employer
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| Required Qualifications: |
A Bachelor’s Degree in data science, statistics, bioinformatics, analytics, or similar field and seven years of experience; Master’s Degree in data science, statistics, bioinformatics, analytics, or similar field and five years of experience; Doctoral Degree in data science, statistics, bioinformatics, analytics, or similar field and three years of experience.
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| Preferred: |
The ideal candidate will possess:
- Demonstrated proficiency in R and/or Python for statistical analysis, simulation, and reproducible research.
- Deep proficiency in Bayesian statistical modeling frameworks (e.g., Stan, PyMC, JAGS), including understanding of sampler diagnostics, convergence properties, and prior specification.
- Strong foundation in causal inference methods (e.g., difference-in-differences, interrupted time series, propensity-based approaches), including the ability to formally state identifying assumptions and assess their plausibility in applied settings.
- Demonstrated experience designing and validating simulation models (e.g., Monte Carlo, agent-based, discrete-event) to evaluate interventions or operational scenarios under uncertainty.
- Experience working with electronic health record data or large-scale clinical datasets.
- Working knowledge of clinical quality improvement, patient safety, or health system operations.
- Solid understanding of AI/ML model evaluation, including calibration assessment, performance monitoring, and validation methodology in applied clinical settings.
- Experience with AI governance concepts, including pre-deployment review and post-implementation evaluation.
- Ability to translate complex analytic findings into clear recommendations for clinical and operational stakeholders who may not have a quantitative background..
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