Research Associate Data Scientist

Job Description

Research Associate Data Scientist (Cedars‑Sinai Medical Center; Los Angeles, CA):
Assist with the development, evaluation, and application of computational and statistical methods, including artificial intelligence and machine learning algorithms and software, for the analysis of biomedical data. Support the presentation and communication of scientific results through laboratory meetings, scientific conferences, and peer‑reviewed publications. Create database‑to‑deployment pipelines for models using programming languages such as Python, R, and C++. Build sustainable data science infrastructure and adhere to best practices for data analysis and machine learning.

Conduct exploratory data analysis to determine the need for advanced analytical methods. Collaborate with senior or lead data scientists, research programmers, and principal investigators to identify opportunities where data science can address biomedical research questions. Test and validate code to ensure robustness of data applications. Participate in the development of innovative algorithms and analytical methods. Evaluate and interpret all analytical methods and results.

Contribute to the oral and written communication of scientific findings, including publications.

Master’s degree or foreign equivalent in Electrical Engineering, Computer Science, Machine Learning, Applied Mathematics, Biomedical Imaging, or a related field, plus three (3) years of experience as a Research Associate Data Scientist, Computer Engineer, Biomedical Data Scientist, or a related occupation.

Must have experience with the following:
Python, C++, and R; developing, testing, validating, and optimizing production‑level, version‑controlled code (Git Hub/Git Lab and Azure Dev Ops) for algorithm development, statistical analysis, and deployment; implementing supervised and unsupervised learning algorithms (random forests, support vector machines, clustering, deep learning), with hands‑on expertise in training, fine‑tuning, and deploying deep learning models using frameworks such as PyTorch and Tensor Flow; building end‑to‑end database‑to‑deployment pipelines including querying large relational databases (SQL), data cleaning, model training, validation, and deploying models in multiple computing environments;

communicating scientific results effectively through peer‑reviewed publications, patents, conference presentations, and internal technical reports; working with medical imaging data, including familiarity with industry‑standard imaging formats (DICOM), image preprocessing workflows (segmentation, denoising, registration, resampling, and normalization), and use of imaging software libraries (SimpleITK, MONAI, or NiBabel) to prepare data for machine learning analysis; managing, processing, and optimizing large‑scale 3D and 4D time‑series datasets for deep learning model development on High‑Performance Computing (HPC) or cloud‑based GPU clusters.

$97,510 – $133,100 per year