Research Associate Data Scientist -Dept of Orthopaedics

at  CedarsSinai

EDUCATION:

• Bachelor’s degree in Computer Science, Machine Learning, Applied Mathematics, Biomedical Imaging, Statistics, Engineering, Physics, or related field, required.
• Master’s degree in these fields is preferred.

EXPERIENCE AND SKILLS:

• Up to two (2) years of professional experience in healthcare industries working with electronic medical record/medical imaging data.
• Experience programming at an intermediate to advanced skill level with a high-level programming language such as Python, C++, or R.
• Experience in healthcare machine learning is preferred.

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 Python, R, C++).
• Creates sustainable data science infrastructure and adheres to data analysis/machine learning standard processes.
• Performs exploratory data analysis to gauge the need for or appropriateness of advanced analytical methods.
• Works with senior or lead data scientists, research programmers, and principal investigators to identify areas where data science can best be applied to answer biomedical research questions.
• Tests and validates code to ensure adaptability of data applications.
• Performs all other duties as assigned.

DEPARTMENT-SPECIFIC RESPONSIBILITIES:

• Participates in the development of innovative algorithms and analytical methods.
• Participates in the evaluation and interpretation of all analytical methods and results.
• Participates in the oral and written communication of scientific results including publications.
• Participates in analytical training activities for faculty, staff, and students.