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Cedars-Sinai's Tatonetti Lab is seeking a Research Associate Data Scientist to contribute to biomedical research using programming, data mining, statistics, and ML. You'll work on data processing, model development, and deployment, with results communicated via publications and conferences.
Requirements include a Bachelor's in a quantitative field; experience with R/Python; familiarity with PHI/HIPAA; strong English skills; and ability to work independently in a collaborative environment.
Join us as we translate today's discoveries into tomorrow's medicine!
The Tatonetti Lab is dedicated to making drugs safer through data analysis. Every day, millions of us or our loved ones take medications to manage our health. We trust these prescriptions to improve our lives and give us hope for a healthier future. However, these drugs can sometimes have harmful side effects or dangerous interactions. Each year, adverse drug reactions affect millions of patients and cost the healthcare industry billions of dollars. At the Tatonetti Lab, we use advanced data science methods, including artificial intelligence and machine learning, to investigate these medicines. By leveraging emerging resources such as electronic health records (EHRs) and genomics databases, we aim to identify for whom these drugs will be safe and effective—and for whom they will not. To learn more, visit Tatonetti Lab at Cedars-Sinai.
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 GitHub to perform repeatable analyses and to train and deploy models to multiple environments.
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