Join Dr. Nicholas Tatonetti's team as a Research Data Scientistat Cedars-Sinai!

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, please visit Tatonetti Lab | Cedars-Sinai.

Are you ready to be a part of breakthrough research?

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. The Research Data Scientist in the Tatonetti Lab at Cedars-Sinai will conduct research to improve drug safety and advance medicine by leveraging clinical and molecular data. This position will lead research from ideation, collaboration, team building, execution, analysis, and communication via peer-reviewed manuscripts and conference presentations.

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:

• 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:

• Develops innovative algorithms and analytical methods
• Evaluates and interprets of all analytical methods and results
• Presents oral and written communication of scientific results including publications
• Conducts project management for active research from ideation through publication and deployment
• 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. Doctorate degree, preferred.

Experience and Skills:

• Two to Five (2-5) years of professional experience in healthcare or pharmaceutical industries working with biomedical data.
• Two to Five (2-5) years of experience programming at an intermediate skill level with a high-level programming language such as R or Python. College projects may be acceptable.
• Two to Five (2-5) years of experience programming at a basic to intermediate proficiency level in SQL.
• Experience in biomedical machine learning, designing rigorous scientific experiments, and writing and publishing peer-reviewed manuscripts 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 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.