Data Scientist

Universities of Wisconsin•Madison, WI

1d•Hybrid

About The Position

The Department of Pediatrics is seeking a Data Scientist to contribute to cutting-edge research in the field of health informatics with a focus in health outcomes, applied machine learning, and high-throughput phenotyping. Join a unique team of physician-scientists, clinical informatics experts, statisticians, engineers, and computer scientists to innovate and develop models that are closely linked to healthcare practice with opportunities to implement your work into real-time and impact patient outcomes. The Data Scientist will work within UW-Madison Pediatric Critical Care focusing on using electronic health record data to improve the care of hospitalized patients. The ideal candidate will be responsible for the development and execution of high-impact machine learning and statistical modeling projects. The individual will use machine learning and computational methods for preprocessing and analyzing data from the electronic health record. We are looking for a candidate who could fill a deep learning or natural language processing subspecialty in our lab. This position will be responsible for providing data for abstract and manuscript submissions. It will also assist with the development of abstracts, posters, presentations, and manuscripts and will be involved in project monitoring and evaluation, data analysis, oversight of trainees, and dissemination of program results. This position may require some work to be performed in-person, onsite, at a designated campus work location. Some work may be performed remotely, at an offsite, non-campus work location.

Requirements

Basic knowledge of software development.
Strong oral and written communication skills.
Exceptional attention to detail.
Ability to work well independently and as part of a team.
Ability to build strong working relationships, communicate technical concepts to non-technical faculty and staff, foster collaborative decision-making and demonstrate professionalism at all times.
Strong organizational and decision-making skills with the ability to set priorities, solve problems, adapt to changes quickly and make independent judgments.
Knowledge and experience with package management and computing environment tools (Venv, uv, Pixi, etc.)
Knowledge and experience developing technical documentation, tutorials, training, or other support materials for end users.
Two or more years of experience with data science and computational workflows.
Experience working with electronic health record datasets.
Experience with data wrangling, cleaning, modeling, visualization, and statistical analysis.
Experience with one or more of the following languages: Python, R, or comparable language(s).
Experience with distributed computing systems such as Linux-based cloud high-performance computing or similar high throughput computing system.
Experience using Git, GitHub and GitLab.

Nice To Haves

Four or more years of experience working in a team-based clinical research environment with highly technical researchers across a variety of methodological fields, research domains, and computational platforms.
Experience with artificial intelligence frameworks (keras, tensorflow, pytorch, JAX, etc.).
Experience providing data support to research teams and contributing to grant proposals.
Experience acting as a consultative resource to research professionals, working with them on projects to understand the desired outcome and developing a roadmap with clear deliverables to get there.

Responsibilities

Independently identifies and implements 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
Documents approaches to address research questions and contributes to the establishment of reproducible research methodologies and analysis workflows
Organizes and automates project steps for data preparation and analysis
Writing of scientific reports, summarizing studies, and presentation of research
Designs, implements, and optimizes end‑to‑end deep learning and natural language processing pipelines, including data ingestion, preprocessing, feature extraction, model development, training, validation, and deployment
Prepares data sets for analysis including cleaning/quality assurance, transformations, restructuring, and integration of multiple data sources
Composes and assembles reproducible workflows and reports to clearly articulate patterns to researchers and/or administrators