Technology is exciting. Technology on behalf of better health? That’s off the charts.

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

The Research Data Scientist in the Tatonetti Lab at Cedars-Sinai will conduct research to improve drug safety by leveraging clinical and molecular data. In the Tatonetti Lab we use advanced data science methods, including artificial intelligence and machine learning, to investigate drug safety, drug-drug interactions, and cancer pharmacology. Using emerging resources, such as electronic health records (EHR) and genomics databases, we are working to advance the science of pharmacology with a focus on serving traditionally underrepresented populations.

To learn more, visit TLab – Data-Driven Drug Safety (tatonettilab.org).

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 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.

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:

• Two to five (2-5) 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 R or Python. College projects may be acceptable.
• Experience programming at a basic to intermediate proficiency level in SQL.
• Experience in biomedical machine learning is preferred.
• Collaborates to problem solve and make decisions to achieve desired outcomes.
• Cultivates and maintains strong customer relationships and rapport with stakeholders and/or client groups.
• Working knowledge of data privacy and security including best practices for data with personal health identifiers (PHI) covered under HIPAA.
• Good interpersonal and communication skills including scientific writing. 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 among individuals and groups.
• Ability to handle multiple demands and/or manage complex and competing priorities.
• Ability to analyze qualitative and quantitative information for research.
• 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.