Data Scientist Position in Arnaout Laboratory

We are hiring a data scientist with mid-career skills and experience. Applications will be accepted on a rolling basis. The position offers an opportunity to participate in cutting-edge research with transformational impact to clinical and research medicine across a wide array of diseases, working with decades of high-quality imaging data alongside clinical domain experts. The position also provides opportunities to publish, present at research conferences, and for professional advancement. Salary and benefits are set according to experience and to UCSF salary scales.


Inquiries should be emailed directly to Dr. Arnaout and should include a CV and a letter of interest clearly but briefly articulating why you are interested in joining the lab.


Requirements:
• strong interpersonal and communication skills
• working with patient data in a HIPPA-compliant and morally and ethically responsible manner
• working closely with an expert interdisciplinary team including both medical and data science professionals
• working independently to complete assigned responsibilities
• strong organizational, record-keeping, written and oral communication skills
• strong motivation to apply pioneering breakthroughs to the practical, personalized everyday care of patients
• A PhD or equivalent degree and experience in computer science or related field
• Experience in neural network design and optimization
• Fluency with Python (ideally using Pandas, Keras, Tensorflow/Theano, etc) and related programming languages, as well as data visualizations (ideally using d3.js, R, Matlab, pylab, seaborn, etc). Specific experience with computer vision projects, and with AWS/cloud computing is a plus


In addition to the above, the successful applicant will:
• be proficient in ‘cleaning’ data of several types (e.g. semi-structured text, images, vectors/matrices)
• be proficient in applying supervised and unsupervised machine learning and other computational techniques (including but not limited to neural networks, discriminant analysis, tree-based methods, boosting, random forests, and support vector machines, as well as [incremental] principal/independent component analysis, t-SNE, and data augmentation techniques) to imaging data
• be proficient in producing excellent data visualizations and analyses for machine learning results
• have a working knowledge of biology and human physiology, and/or a desire to learn relevant concepts