JOB SUMMARY:

About Us: 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 experienced Research Data Scientists for the Data Science to Promote Precision Medicine initiative, and drive transformative data science projects to enhance healthcare and research at SMPH.
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 Data Scientist, the incumbent will use clinical, omics and imaging data to develop and implement advanced computational algorithms and support conduct of groundbreaking data-driven research.

On a day-to-day basis, the incumbent could expect to engage in the following activities in supporting researchers:

• Develop and implement informatics pipelines for clinical, omics or imaging data processing, integration, and visualization.
• Analyze and interpret large-scale omics or imaging datasets using advanced computational/bioinformatics methods.
• Apply statistical and machine learning techniques to identify patterns and insights from complex clinical and biological data.
• Develop/validate advanced computational algorithms using Artificial Intelligence (AI), Machine Learning (ML), regression, and rules-based models.
• Build predictive models to forecast disease risk and progression, health outcomes and treatment effectiveness; and gain actionable insights from model outputs and communicate findings to research community.
• Apply NLP techniques to extract insights from clinical notes, reports, and unstructured text data; and develop models for sentiment analysis, entity recognition, and information extraction.
• Work closely with cross-functional teams, including clinicians, data scientists, data engineers, and product managers, and present research findings and recommendations in a clear and actionable manner.
• 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.

EDUCATION:

Preferred
Master’s Degree
Preferred in Computer Science, Data Science, Clinical Informatics, Bioinformatics Epidemiology, Biostatistics, Ophthalmology or related disciplines.

QUALIFICATIONS:

Required:

• At least three years of experience in any of the following:
• Analysis of large-scale human sample-based omics data (genomics/proteomics/metabolomics) and strong knowledge of bioinformatics tools and databases.
• Analysis of EHR data
• Analysis of Ophthalmic imaging data
• Proficiency in programming languages such as Python, R, and SQL.
• Experience with statistical analysis and machine learning techniques.
• Excellent problem-solving skills and attention to detail.
• Strong communication skills and ability to work collaboratively in a team environment.

Preferred:

• Experience working in academic institutions.

Contributes to a research agenda set by a lead researcher by preparing data sets, analyzing them using data science techniques, and presenting the results. May work independently or as part of a team.

• 15% Prepares data sets for analysis including cleaning/quality assurance, transformations, restructuring, and integration of multiple data sources
• 15% 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
• 15% Organizes and automates project steps for data preparation and analysis
• 15% Composes and assembles reproducible workflows and reports to clearly articulate patterns to researchers and/or administrators
• 20% Documents approaches to address research questions and contributes to the establishment of reproducible research methodologies and analysis workflows
• 20% Develops and optimizes advanced computational algorithms using Artificial Intelligence (AI), Machine Learning (ML), regression, and rules-based models