Research Data Scientist

Universities of Wisconsin•Madison, WI

1d•$100,000•Remote

About The Position

Are you passionate about improving patient health outcomes, optimizing healthcare processes, and advancing science? Join our dynamic Informatics and Information Technology team at the University of Wisconsin School of Medicine and Public Health in Madison, Wisconsin. We are committed to revolutionizing healthcare through implementation of advanced data science approaches, conducting cutting-edge data-centric research, and generating real-world evidence to improve patient health outcomes in WI. As a Research Data Scientist, the incumbent will use clinical data to develop and implement advanced computational algorithms and to support groundbreaking data-driven research. This role is critical to integrating disparate data domains; you will architect multi-modal analysis pipelines and serve as a primary consultant for researchers, helping them navigate complex study designs and analysis. On a day-to-day basis, the incumbent could expect to engage in the following activities in supporting researchers: Multi-Modal Pipeline Development: Develop and implement informatics pipelines for the processing, integration, and harmonization of heterogeneous data sources (e.g., joining unstructured clinical notes with medical imaging and structured diagnostic codes). Statistical Modeling & Hypothesis Testing: Develop predictive models using retrospective real-world data to estimate disease risk, progression, and treatment effectiveness, while addressing bias and fairness. Design and execute rigorous hypothesis testing on observational datasets to validate research findings. Imaging Analysis: Analyze imaging datasets using advanced computational methods and Vision Models (e.g., CNNs, Transformers) to identify patterns in complex biological data. NLP & EHR Analysis: Leverage modern NLP frameworks and LLMs to extract critical insights from unstructured clinical notes and reports, ensuring data quality and integrity through rigorous preprocessing. Consultation & Capacity Building: Act as a technical consultant for the research community, translating complex model outputs into actionable scientific insights and helping investigators select appropriate analytical strategies. Work closely with data governance and security to ensure compliance with privacy regulations (e.g., NIST, HIPAA) when working with healthcare data; and address bias and fairness issues in AI models when dealing with sensitive health data. Keep abreast of emerging trends and advancements in AI research to propose innovative solutions to healthcare challenges. Additional position details: It is anticipated that this position will be remote and requires work be performed at an offsite, non-campus work location. Terminal, 24 month position. This position has the possibility to be extended or converted to an ongoing appointment based on need and/or funding. The UW School of Medicine and Public Health (SMPH) is a leader in research and innovation, dedicated to improving patient outcomes through advanced data science and analytics. We are seeking an experienced Research Data Scientist for the Data Science to Promote Precision Medicine initiative and drive transformative data science projects to enhance healthcare and research at SMPH. This position is within the School of Medicine and Public Health’s Office of Informatics and Information Technology (IIT). IIT is a multidisciplinary team of data scientists, engineers, developers, and IT support staff. We offer a variety of Informatics and IT services to departments and research staff within the School of Medicine and Public Health and beyond to support the conduct of high-quality clinical and translational research. Informatics: We provide innovative solutions and training for a broad spectrum of clinical and translational research utilizing real-world data to facilitate rapid translation of research findings into clinical practice, with an emphasis on precision medicine, healthcare delivery, and population health. Technology Solutions: We provide technology solutions to the School of Medicine and Public Health including cybersecurity, educational technology, and IT support.

Requirements

Biostatistics, Machine Learning, & Experimental Design: A strong foundation in biostatistics, machine learning, and study design, specifically applied to observational research or clinical trials.
Note for Assessment: Candidates must provide demonstrated evidence of this skill through a track record of peer-reviewed scientific publications where they contributed or performed the statistical methodology, or a portfolio of projects showcasing rigorous experimental design.
Specialized Technical Experience: Demonstrated experience in at least one of the following areas: Medical Imaging: Experience with Vision Models (e.g., ResNet, EfficientNet, ViT, MONAI) for classification, segmentation, or detection tasks. NLP/EHR Data: Experience applying NLP techniques (e.g., Transformers, spaCy, LLMs) to extract insights from unstructured text or Electronic Health Records.
Programming: Proficiency in Python and deep learning frameworks (PyTorch, PyTorch Lightning, or TensorFlow/Keras).
Excellent problem-solving skills and attention to detail.
Strong communication skills and ability to work collaboratively in a team environment.

Nice To Haves

Experience working in academic institutions.
Knowledge of healthcare data standards, common data models, and terminologies (e.g., OMOP, mCode, FHIR, HL7, ICD-10, CPT).
Familiarity with R for statistical analysis and SQL for data querying.
Familiarity with 'omics data analysis or bioinformatics workflows.
Experience packaging data science solutions into reproducible tools, APIs, or applications for use by a broader research community.

Responsibilities

Organizes and automates project steps for data preparation and analysis
Documents approaches to address research questions and contributes to the establishment of reproducible research methodologies and analysis workflows
Prepares data sets for analysis including cleaning/quality assurance, transformations, restructuring, and integration of multiple data sources
Serves as an institutional subject matter expert and liaison to key internal and external stakeholders regarding data science best practices and methodologies and represents the interests of data science
Identifies and implements or guides others in implementing appropriate data science techniques to find data patterns and answer research questions chosen by the lead researcher including data visualization, statistical analysis, machine learning, and data mining
Develops and optimizes advanced computational algorithms using Artificial Intelligence (AI), Machine Learning (ML), regression, and rules-based models
Composes and assembles reproducible workflows and reports to clearly articulate patterns to researchers and/or administrators