Research Data Scientist - Artificial Intelligence in Medicine Research Center

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

Our mission in the Artificial Intelligence in Medicine Research Center at Cedars‑Sinai is to use AI to help solve existing gaps in mechanisms, diagnostics, risk assessment and therapeutics for major human disease conditions. Faculty and trainees in the center function as custodians of the patient’s healthcare interests and harness Cedars-Sinai's clinical data warehouse and data from collaborating sites to design questions that are ethically vetted, evaluated, validated and implemented. The center consolidates AI, clinical validation and innovation under one umbrella. Using a disease-based approach enables cross-disciplinary connections among clinicians, scientists and trainees on multiple levels. To learn more, visit Cedars-Sinai | AIMRC.

Are you ready to be a part of breakthrough research?

The Research Data Scientist participates in biomedical research projects using programming, data mining, statistics, machine learning, and visualization techniques to lead the development, evaluation, and/or application of algorithms, methods, and software for data analysis. Responsibilities include querying databases, data processing, data cleaning and quality control, supervised and unsupervised machine learning, parametric and nonparametric statistical methods, deploying production models, and leading the 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:

• Leads 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.
• Leads 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, neo4j).
• Creates sustainable data science infrastructure and adheres to data analysis/machine learning best practices.
• Performs data cleaning, quality control, exploratory data analysis to gauge the need for or appropriateness of advanced analytical methods.
• Leads discussions with senior and/or lead research 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.