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Data Scientist

PHC Data Science Imaging group – South San , Francisco

Management, or General

Salary : $50000 - $100000  / YEAR

Responsibilities: Collaborate with internal imaging- and data scientists and external vendors to derive and validate novel imaging biomarkers in support of clinical drug development and RWD evidence (payer support) generation Curate/clean/organize large and messy clinical imaging datasets Identify and support imaging data management solutions within PHC Continually search for opportunities to automate workflows and streamline processes Support and contribute to the development of advanced analytics and computational tools   Required skills: In-depth knowledge and coding experience in Python (polyglot in multiple programming languages a plus). Hands-on skills in Data Science packages, for instance Pandas, Scikit-learn, and/or numpy, a must. Extensive experience with commonly used Deep Learning models (2d/3d CNN, LSTM/GRU, etc), modern DL architectures (Resnet, U-net, etc), and frameworks (Tf, pytorch, keras, etc). Hands-on on other ML algorithms (RF, GBM, etc) a plus. Familiarity with advances in AI research and related applications in medical imaging, and/or computer vision. Technical and organizational skills/experience to lead complex, end-to-end ML/DL/AI projects, including typical project stages such as: data engineering, computing/storage resource budgeting, model training, model selection, model evaluation, and communication with other stakeholders. Fluent in using scientific computing environment e.g. unix / linux shell in a HPC cluster on premise or in cloud, to accomplish common development tasks (eg. editing, testing, efficient debugging, etc.)  Hands-on experience with productivity toolchains (eg JIRA, enterprise git.) Understand the practical aspect of the mathematical foundation of ML, in particular optimization (first order method eg gradient descent, second order method eg Newton-Raphson, why in DL first order is dominant). Understand the practical aspect of statistics (population vs sample, different sampling techniques, etc) PhD or MS in relevant quantitative field (CS, EE, Physics, Mathematics, Statistics, etc.), and/or adv. Life Sciences degree with significant computational experience > 2yr post-graduate work-experience in fields such as engineering, research, or product development with responsibilities relevant to position. Publications in the areas of Deep-/Machine Learning, and/or Statistics a plus. Solid understanding of medical image data formats (eg DICOM) Excellent communication skills Ability to multitask and prioritize while maintaining efficiency and quality of work Internally motivated with a commitment to accuracy and quality

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