Research Associate Data Scientist - Omar Lab - Computational Biomedicine

Research Associate Data Scientist

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.

The Omar Lab is dedicated to developing robust AI-powered tools to forecast the risk of adverse clinical outcomes in cancer patients at early stages. We use standard clinical data—primarily histopathology and radiology images—to build cost-effective, accurate tools for personalized cancer risk assessment and management. Our research leverages the tumor microenvironment to extract high-quality spatial and molecular features that guide our training process, guarding against overfitting.

Are you ready to be a part of breakthrough research?

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 Git Hub 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.
• Keeps appropriate analytical records, documentation, and software version control.
• 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 in R, Python, or Linux bash and genetics and genomics data analysis preferred.
• 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.
• Demonstrates 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…