Join Dr. Joshua Levy's team and contribute to data-driven solutions as a Research Data Scientist (Per Diem) at Cedars-Sinai!

Dr. Levy?s research focuses on implementing AI, digital pathology, and statistics in various fields of anatomic pathology. Dr. Levy?s work has resulted in the development of several computational methods, including tools that measure spatial molecular variations in tumors. Those variations can help physicians more accurately determine the severity of patients? disease.

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, and 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.

Education:

• A bachelor's degree in computer science, machine learning, applied mathematics, econometrics, statistics, engineering, physics, or a related field is required. A master's degree is preferred.

Experience and Skills:

• Two (2) years of professional experience in healthcare or pharmaceutical industries working with biomedical data is required.
• Two (2) years of experience programming at an intermediate skill level with a high-level programming language such as R or Python is required. College projects may be acceptable.
• Two (2) years of experience programming at a basic to intermediate proficiency level in SQL is required.
• 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 healthcare 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 analyze qualitative and quantitative information for decision support.
• High level of proficiency in Microsoft Windows and other Microsoft Office software, such as 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.