Business Intelligence Architect - Internal Medicine

UIOWA•Iowa City, IA

12d

About The Position

Work closely with clinical research and data analysis teams to gather, analyze, and interpret complex healthcare data sets under supervision from senior data scientists and research managers. Extract relevant data from relational database systems in clinical data warehouses using SQL, apply expertise in statistical analysis by using R, Python, or similar tools, and contribute to research studies at the University of Iowa Carver College of Medicine and the Iowa City Veteran Affairs Medical Center.

Requirements

A Master’s degree or an equivalent combination of education and experience is required.
Substantial experience in complex data analysis using software/languages such as R, Python, or similar is required.
Mastery of advanced statistical modeling such as generalized linear regression, mixed-effect generalized linear regression, and Cox regression models is required.
In-depth knowledge of relational database systems and proficiency in SQL query optimization is required.
Excellent analytical and problem-solving skills are required.

Nice To Haves

Ph.D. completed or near completion in a quantitative field is preferred
Experience in trial design and/or causal inference and mediation methodology is desired.
Experience with machine learning, predictive modeling, and advanced frameworks (e.g., multi-task learning, deep learning) is desired.
Experience with text classifications and natural language processing is desired.
The ability to work both independently and collaboratively in a fast-paced, multi-disciplinary research team is desired.
The ability to provide functional and/or administrative supervision of staff and mentor junior-level analysts is desired.

Responsibilities

Utilize Python, R, or SAS (or similar programming languages) to clean, preprocess, analyze, and visualize large datasets to extract meaningful insights.
Implement reproducible analysis pipelines, ideally using Git/GitHub-based collaborative development workflows, adhering to open-science practices.
Perform advanced data analysis techniques.
Design and optimize SQL queries to retrieve, manipulate, and transform data from relational databases and other data sources.
Perform advanced modeling including multi-task learning, sequence/time-series forecasting, survival/event-time analysis, and risk stratification for health services research studies.
Conduct high-dimensional causal inference and mediation analysis to map pathways linking exposures, confounders, and clinical mediators to health outcomes.
Evaluate and audit clinical ML and NLP models for calibration, uncertainty estimation, interpretability, and subgroup fairness.
Apply advanced statistical and machine learning techniques to develop longitudinal predictive models and clinical algorithms.
Work closely with health services investigators on the initiation of new research projects and the development of study design and analytic plans for grant proposals.
Prepare scientific manuscripts and reports jointly with a team of health services investigators.
Ensure data quality and integrity through data validation, cleaning, and error detection.
Stay updated on the latest developments in data science, statistical analysis, and relevant technologies.
Mentor junior-level analysts, serve as a technical lead, and drive technology roadmaps for data operations leveraging your organizational leadership experience.