Top Healthtech Data Science Jobs in San Francisco, CA

As a Staff Data Scientist, you'll analyze and enhance search and ranking systems for mental health provider matching, collaborating with cross-functional teams to improve patient access to care.

The Senior/Staff Data Scientist will analyze healthcare data to derive insights, support internal teams with analytics, and lead data function standards and processes.

The Scientist II will carry out complex research projects, executing data analyses and communicating scientific findings for biopharmaceutical clients using the Tempus data platform.

The Senior Data Scientist will design and execute experiments to inform patient care and company growth, mentoring teammates and implementing statistical rigor.

Lead data science initiatives focusing on healthcare fraud, waste, and abuse. Perform detection analysis, assess trends, and present findings to support decision-making.

Seeking a Senior Data Scientist to leverage AI and machine learning in healthcare datasets. Responsibilities include developing algorithms, leading projects, and mentoring juniors while collaborating with technical and business teams.

Analyze omics datasets, design high-throughput experiments, integrate multimodal data, and collaborate with biologists to understand disease mechanisms and identify therapeutic targets.

The role involves analyzing microscopy data, collaborating with biologists to design experiments, and utilizing machine learning and bioinformatics to uncover disease mechanisms and therapeutic targets.

The Associate Principal Statistical Analyst will oversee statistical programming for oncology clinical trials, mentor others, ensure compliance with standards, and deliver analyses for regulatory submissions.

The Data Scientist role involves analyzing complex datasets, developing machine learning models, collaborating with cross-functional teams, and contributing to data strategies for improving cancer care.

The Principal Data Scientist analyzes real-world data to develop machine learning models, leads studies, and collaborates with various teams to optimize devices in diabetes care.