Zuckerberg San Francisco General Hospital’s predictive analytics team hires an ML postdoctoral fellow and/or a senior data scientist to join them.
Zuckerberg San Francisco General Hospital (ZSFG) is at the forefront of applying artificial intelligence and machine learning (AI/ML) to help improve outcomes in vulnerable and underserved populations. Our team is dedicated to developing and testing ML algorithms that support hospital performance improvement efforts, emphasizing health equity and algorithmic fairness. We are committed to translating these algorithms into clinical practice and embedding all our algorithms into various clinical systems, including the electronic health record (EHR), for rigorous testing and monitoring.
RESPONSIBILITIES
The primary responsibilities are:
Develop and test new ML algorithms that analyze structured data and clinical notes from the electronic health record (EHR) system.
Research and assess the use of large language models (LLMs) to develop interpretable and scalable clinical decision support systems
Be up-to-date on state-of-the-art methodologies in the relevant technical fields and application domains.
Ensure that the developed ML algorithms are reliable and fair
Publish research manuscripts in collaboration with the team
Our predictive analytics team is highly collaborative and includes team members with wide-ranging expertise, including healthcare, clinical IT, machine learning, biostatistics, and computer science.
QUALIFICATIONS
The position requires a Ph.D. or equivalent in data science, (bio)statistics, computer science, or another relevant field.
Able to work collaboratively with a team
Has experience in training and testing ML algorithms for large datasets
Has experience in methodological development and can perform independent research with a strong and relevant publication record
Has an interest in analyzing Electronic Health Record (EHR) data and natural language processing (prior experience is a huge plus)
Has strong software engineering background (python, SQL, spark, DuckDB, git, high-performance computing, etc)
Screening of applicants will begin immediately and continue as needed throughout the recruitment period. If you are interested, please submit the following materials to; jean.feng@ucsf.edu
Ref: at https://www.jeanfeng.com/joining.html
- Cover letter
- Updated CV (summarizing your education and work experience).
- The names and email addresses of three references
- A code sample on Github
- One representative publication
Best wishes
Jean Feng
Assistant Professor in Residence
Department of Epidemiology and Biostatistics
University of California, San Francisco
https://www.jeanfeng.com/