Director of AI Medical Science
We're looking for a world-class Director of AI who wants to tackle humanity's most challenging problems. What will you do: • This position offers you the opportunity to craft AI approaches to solving particular medical problems, with the goal of improving and inventing new & effective clinical workflow. • You will build, manage and mentor a team of 4-6 Data Scientists, setting and maintaining high standards of execution within the team. • Construct and curate large problem-specific datasets. • Design and implement AI and ML techniques aimed at solving specific problems. • Lead the development of production software that utilizes AI and ML methods and technology to implement digital care products and services. • This position requires you to keep up with the latest AI research and collaborate with diverse teams (internal and external), including AI researchers, clinical and research physicians, medical imaging researchers and software architects.. • Generate high-quality patents and top tier technical/clinical publications, and transfer technology into products. Requirement
Experience building and managing a team of Data Scientists, Data Engineers and Data Analysts
5+ years' experience working in startups and/or technology field
A PhD or Masters in Biostatistics / Bioinformatics / Biomedical Engineering or a related field with strong hands-on software development / programming experience.
5+ years of relevant work experience in AI / ML medical projects, medical imaging or statistical learning
A track record of research (publication) excellence and/or significant product development..
Excellent knowledge and development experience of common AI / ML frameworks and packages (Tensorflow, PyTorch, Keras, PyTorch, scikit-learn, SpaCy. etc.).
Excellent rapid prototyping skills with medical applications using Python; C++, Matlab.
Prior experience working with physicians to identify (novel) important problems and assessing possible deep learning solutions is a plus.
Good understanding and experience of ML modeling lifecycle and pipelines, including data preparation, data wrangling, ML model development, model training, model performance measurement and tuning..
Knowledge and experience with statistical Natural Language Processing (NLP) methods and technologies is desired. Experience with models such as Support Vector Machines (SVM), Convolutional Neural Networks (CNNs), Long Short Term Memory (LSTM), etc.
Understanding of software engineering principles and methods, agile software development methodologies and modern development processes.
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