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Higher Education, Healthcare, Research, Biotechnology

Data Scientist

San Francisco, California, United StatesOnsiteFull TimePosted 2 months agoVisa sponsorship available

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Role summary

The Division of Cardiology at Zuckerberg San Francisco General Hospital seeks a Data Scientist to develop AI/ML algorithms for clinical decision support and hospital performance improvement. This role requires a strong background in computer science or statistics, with a proven track record in algorithm development and application. Experience with Linux, Git, Python/R, and high-performance computing is essential. Preferred qualifications include experience with EHR data, data warehousing, and developing large language models. The position involves working collaboratively within a multidisciplinary team to advance AI/ML applications in healthcare for underserved populations.

Zuckerberg San Francisco General Hospital and the Division of Cardiology is at the forefront of applying artificial intelligence and machine learning (AI/ML) to help improve outcomes in vulnerable and underserved populations. We currently have a successful and growing ML predictive analytics team consisting of ML researchers, clinicians, informaticists, and biostatisticians. We seek a data scientist to join our team to develop AI/ML-based algorithms to support clinical decision making, hospital performance improvement efforts, and more. This position requires experience in computer science, (bio)statistics, or another relevant field. A proven track record of productivity within academia or industry in publishing and/or developing and applying new algorithms is necessary for this role. The candidate should have experience developing in a Linux environment, using git-based workflows, writing in Python/R, using high-performance computing, and working collaboratively with a multidisclinary team. Ideally, the candidate also has experience cleaning and analyzing Electronic Health Record (EHR) data, creating datawarehouses for data analytics, and experience/interest in developing new ML algorithms including large language models.

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