Job Abstract

The Cardiovascular Data Science (CarDS) Lab at Yale University has an opening for a Data Scientist to participate in a series of projects that focus on leveraging healthcare data to improve patient care. The work spans work with structured and unstructured data in the electronic health record (EHR), with the opportunity to work on applications of machine learning/deep learning in novel areas of healthcare. The position is open for a driven individual with either a master’s or doctoral training in data science, computer science, or a similar background with some experience working with large datasets. Prior experience with healthcare is not required but will be helpful. The ideal candidate will have an interest in broad career development in a dynamic environment that allows them to develop as a leader in healthcare data science and innovation.

The team at CarDS lab represents a multidisciplinary and exciting group of postdoctoral trainees and graduate and undergraduate students across Yale, with broad collaboration with informatics, computer science, and statistics groups at Yale and at several leading institutions nationally. There will also be a close collaboration with the Yale-New Haven Hospital Center for Outcomes Research and Evaluation (CORE). Our team has developed novel tools to measure and improve the quality of care using data from the EHR and software solutions for the early diagnosis of several cardiovascular disorders. The research in the lab allows unique collaborative opportunities with industry and health technology partners. Under the direction of the Principal Investigator, the ideal candidate will perform a variety of duties involving the development of data and analytic pipelines for research studies and will work as a member of a research team to provide input in the design of the study, perform data analysis, and lead or assist drafting analytical sections for peer-review publication for various projects. The candidate is expected to lead several efforts, including working with the team to develop, discover and apply novel machine learning applications to healthcare. Responsibilities will also include participating in the design, implementation, and maintenance of data pipelines and leading/assisting in building algorithms for deep learning with close collaboration from the study team.

While programming experience with python and/or R is required, experience with one or more of the following skills will be an asset. However, if the individual is willing to learn these skills, there are opportunities to learn them in this position: distributed and cluster computing, with a specific focus on PySpark, working with large tabular data with python/R, basic principles of natural language processing and their applications in python with PyTorch/Huggingface/SpaCy, applications of computer vision and signal processing in Tensorflow or PyTorch, and the ability to deploy and work in containerized environments.

Develop and execute new and/or highly complex algorithms and statistical predictive models and determine analytical approaches and modeling techniques to evaluate potential future outcomes. Establish analytical rigor and statistical methods to analyze large amount of data, using advanced statistical techniques and mathematical analyses. Manage analytical projects from data exploration, model building, performance evaluation, through implementation. Develop work plans and monitor progress and project timelines. Document coding and changes to work plans using established work group methods in GitHub. Interact with a multidisciplinary team of internal and external peers to regularly, effectively, and openly communicate progress and outcomes of planned work. Attend weekly team meetings to discuss team and project-related activities, issues, change, communications, and updates.
Strong organizational, time management, and leadership skills. Ability and willingness to work in a highly collaborative team environment and matrixed organization. Develop and execute new and/or highly complex algorithms and statistical predictive models and determine analytical approaches and modeling techniques to evaluate potential future outcomes. Establish analytical rigor and statistical methods to analyze large amount of data, using advanced statistical techniques and mathematical analyses. Manage analytical projects from data exploration, model building, performance evaluation, through implementation. Develop work plans and monitor progress and project timelines. Document coding and changes to work plans using established work group methods in GitHub. Interact with a multidisciplinary team of internal and external peers to regularly, effectively, and openly communicate progress and outcomes of planned work. Attend weekly team meetings to discuss team and project-related activities, issues, change, communications, and updates. Master’s Degree in computer science, applied/computational mathematics, engineering, biostatistics, statistics, or a quantitative field such as astronomy or geology, economics, health policy, health services research, public policy, or a related field and two years of demonstrated experience or an equivalent combination of education and demonstrated experience.