Join us as we translate today's discoveries into tomorrow's medicine!

The Department of Computational Medicine (CBM) is a robust infrastructure established to develop, evaluate, and apply cutting-edge computational and statistical methods and software for the analysis of biomedical and clinical data across the Cedars-Sinai enterprise.

Are you ready to be a part of breakthrough research?

The URBS-lab focuses on the development and application of machine learning, artificial intelligence, and statistical methodologies targeting a variety of domains and data types within biomedical research. Emphasis is placed on developing methods that are interpretable/explainable, scalable to ‘big data’, and capable of detecting complex patterns of association. Current research focuses on automated machine learning, rule-based modeling, feature selection, and evolutionary optimization. Other areas of interest include (but are not limited to) rare-variant analysis, data simulation, data integration, heterogeneous patient subgroup identification, time-series analysis, deep learning, and identifying and correcting for biases. We are a highly collaborative lab with access to EHR, genomic, and other ‘omics’ data in areas of research such as obstructive sleep apnea, congenital heart disease, pancreatic cancer, transplantation donor-recipient matching, and hospital readmission. To learn more, please visit Urbanowicz Research Lab | Cedars-Sinai.

The Research Associate Data Scientist participates in biomedical research projects using programming, data -mining, statistics, machine -learning, and visualization techniques to develop, evaluate, and/or apply algorithms and software for data analysis. Responsibilities include querying databases, data processing, supervised and unsupervised machine learning, deploying production models, and communication of scientific findings via peer-reviewed publications and scientific conferences. Writes clean, performant, reusable code managed on GitHub to perform repeatable analyses and to train and deploy models to multiple environments.

Primary Duties & Responsibilities:

• Assists with the development, evaluation, and/or application of computational and statistical methods including artificial intelligence and machine learning algorithms and software for the analysis of biomedical data.
• Assists with the presentation and communication of scientific results through laboratory meetings, scientific conferences, and peer-reviewed publications.
• Creates database-to-deployment pipelines for models using the necessary programming languages (primarily R, Python, SQL).
• Creates sustainable data science infrastructure and adheres to data analysis/machine learning best practices.
• Performs exploratory data analysis to gauge the need for or appropriateness of advanced analytical methods.
• Works with senior or lead data scientists and principal investigators to identify areas where data science can best be applied to answer biomedical research questions.
• Tests and validates code to ensure robustness of data applications with version control through GitHub.
• Performs all other duties as assigned.

Department-Specific Responsibilities:

• Participates in the development of innovative algorithms and analytical methods.
• Participates in the evaluation and interpretation of all analytical methods and results.
• Participates in the oral and written communication of scientific results including publications.
• Participates in analytical training activities for faculty, staff, and students.

Education:

• Bachelor's degree in Computer Sciences, Machine Learning, Applied Mathematics, Econometrics, Statistics, Engineering, Physics, or related field, required. Master's degree, preferred.

Experience and Skills:

• Up to two (2) years of professional experience in healthcare or pharmaceutical industries working with biomedical data.
• Experience programming at an intermediate skill level with a high-level programming language such as Python. College projects may be acceptable.
• Experience programming at a basic to intermediate proficiency level in SQL.
• Experience in biomedical machine learning is preferred.
• Working knowledge of data privacy and security including best practices for data with personal health identifiers (PHI) covered under HIPAA.
• Strong interpersonal and communication skills. And has full command (verbal and written) of the English language.
• Demonstrated commitment to customer service and an ability to meet the needs and expectations of patients and health care colleagues.
• Demonstrated success working independently, forging relationships, and managing multiple tasks with minimal directions.
• Ability to promote and foster participation/collaboration among individuals and groups.
• Ability to handle multiple demands and/or manage complex and competing priorities.
• Ability to analyze qualitative and quantitative information for decision support.
• High level of proficiency using Microsoft Windows and other Microsoft Office software: MS Excel, Outlook, Powerpoint Word, etc.
• Must be able to manage competing priorities, while being extremely adaptable and flexible and maintaining a positive work environment.