The Center for Advanced Medical Analytics (CAMA) in the Cardiology Division of the University of Virginia School of Medicine is seeking a talented and passionate Data Scientist and Data Manager to perform research on projects related to predictive monitoring using continuous physiological signals and clinical data. CAMA has collected a large repository of physiologic data and annotated clinical events that has not only produced a high volume of academic publications, but has resulted in the development of applications and displays being actively used and tested out clinically with UVA Health System partners. A data manager is needed to curate this collection of databases. These resources are uniquely ideal for implementing deep learning and other modern machine learning techniques. Candidates are sought who area able to provide data management as well as perform supervised and unsupervised learning methods, such as convolutional neural networks, recurrent neural networks, autoencoders, random forests and logistic regression. Candidates must have expertise in at least one of Matlab, Python, or R, and preferably should be proficient in programming, using at least 2 or more of Matlab, Python, R and other programs and associated machine learning libraries and packages. Candidates should have experience in the application of machine learning techniques to physiological data. Analytics support is needed for research on physiological monitoring including: advanced mathematical and statistical analysis in order to develop and deploy algorithms for early detection of illness, large-scale time series and other data acquisition. Qualified applicants must have a Master's degree or higher in Data Science, Computer Science, Mathematics, Systems Administration, Systems Engineering, Electrical Engineering, or other engineering disciplines at time of employment. A formal background in mathematics and statistics is preferred.

In addition, candidates should have excellent oral and written communication skills with the ability to present at professional meetings. The ability to work with undergraduate and graduate students on multiple projects along with the PI and faculty members in the Center for Advanced Medical Analytics is required.

This position is held within the Department of Medicine, Division of Cardiovascular Medicine.  The term of this position is one year with the possibility of renewal, based on funding and satisfactory performance.

The successful candidate will:

• 75%: Perform scientific programming for mathematical analysis of streaming physiological monitoring data.
• 225%: Manage large, complex data sets and utilizes high performance computing resources as needed.
• Work closely with physicians and other medical experts to understand clinical significance of projects.
• Collaborate with other members of the large clinical and engineering team.

Qualified candidates must have:

Education: Master's Degree or higher in Data Science, Computer Science, Mathematics, systems administration, Systems Engineering, Electrical Engineering, or other engineering disciplines
Experience: Three years of relevant experience including expertise in one of Python, R, or Matlab.

Preferred qualifications: Experience in any of the following fields: bioinformatics, computational statistics, data analytics, signal processing. Experience with at least two of the following programming languages: C++, Python, R, Java, and Matlab.

Required knowledge, skills, and abilities:

• Ability to be self-motivated.
• Ability to work well with teams.
• Ability to serve as a technical liaison.
• Ability to manage large, complex data sets across multiple sites.
• Ability to perform scientific programming for mathematical analysis of streaming physiological monitoring data.

This position will remain open until filled.  This is an exempt level, benefited position. For more information on the benefits at UVA, visit hr.virginia.edu/benefits. This position is a restricted position and is dependent upon project need, availability of funding, and performance. This position is located in Charlottesville, VA.

The University will perform background checks on all new hires prior to employment.  A completed pre-employment health screen is required for this position prior to employment.

To Apply:

Please apply through Workday, and search for R0043329. Internal applicants must apply through their UVA Workday profile by searching ‘Find Jobs.’ Complete an application online with the following documents:

•         CV

•         Cover letter

Upload all materials into the resume submission field, multiple documents can be submitted into this one field. Alternatively, merge all documents into one PDF for submission. Applications that do not contain all required documents will not receive full consideration.

References will be completed via UVA’s standardized process Skill Survey. A total of five references will be requested via SkillSurvey during the final phase of the interview process.

For questions about the application process, please contact Jessica Russo, Recruiter, at sxv9zv@virginia.edu.

For more information about UVA and the Charlottesville community please see http://www.virginia.edu/life/charlottesville and https://embarkcva.com/.

COVID Vaccination Requirement and Guidelines

Please visit the UVA COVID-19 Job Requirements and Guidelines webpage prior to applying for current information regarding vaccination requirements and guidelines for employment at UVA.

The University of Virginia, including the UVA Health System which represents the UVA Medical Center, Schools of Medicine and Nursing, UVA Physician’s Group and the Claude Moore Health Sciences Library, are fundamentally committed to the diversity of our faculty and staff.  We believe diversity is excellence expressing itself through every person's perspectives and lived experiences.  We are equal opportunity and affirmative action employers. All qualified applicants will receive consideration for employment without regard to age, color, disability, gender identity or expression, marital status, national or ethnic origin, political affiliation, race, religion, sex (including pregnancy), sexual orientation, veteran status, and family medical or genetic information.