Lead and execute data-driven projects to drive insights and innovation in biotech research and development; Utilize cutting-edge ML/AI techniques to identify, implement, and advance state-of-the-art models, contributing to the enhancement of core platform capabilities and the acceleration of CRISPR-based drug innovation; Design and develop predictive/generative models and algorithms to support CRISPR-based drug discovery and gene editing medicine initiatives; Engage in collaboration with scientists, biologists, and curators to engineer ML features, enabling the interpretation of ML results within the context of the field; Drive the development and implementation of data strategies based on best practices, ensuring the security, accessibility, and interpretability of Scribe's proprietary data
Requirements:
PhD in Computer Science, Data Science, Applied Mathematics, Statistics, Computation Biology or equivalent engineering fields; Demonstrated expertise with machine learning technologies and a proven track record of implementing deep learning architectures, either in industry or academia, for a minimum of 3 years; Proficiency in utilizing various tools and frameworks for machine learning and data science, such as PyTorch, Scikit-Learn, Tensorflow, Keras, AWS SageMaker, and others; Hands-on experience in ML/AI-based protein modeling, including familiarity with AlphaFold2, RoseTTAFold, ESM2, and similar technologies
Text:
Data Scientist/Sr. Data Scientist Lead and execute data-driven projects to drive insights and innovation in biotech research and development; Utilize cutting-edge ML/AI techniques to identify, implement, and advance state-of-the-art models, contributing to the enhancement of core platform capabilities and the acceleration of CRISPR-based drug innovation; Design and develop predictive/generative models and algorithms to support CRISPR-based drug discovery and gene editing medicine initiatives; Engage in collaboration with scientists, biologists, and curators to engineer ML features, enabling the interpretation of ML results within the context of the field; Drive the development and implementation of data strategies based on best practices, ensuring the security, accessibility, and interpretability of Scribe's proprietary data PhD in Computer Science, Data Science, Applied Mathematics, Statistics, Computation Biology or equivalent engineering fields; Demonstrated expertise with machine learning technologies and a proven track record of implementing deep learning architectures, either in industry or academia, for a minimum of 3 years; Proficiency in utilizing various tools and frameworks for machine learning and data science, such as PyTorch, Scikit-Learn, Tensorflow, Keras, AWS SageMaker, and others; Hands-on experience in ML/AI-based protein modeling, including familiarity with AlphaFold2, RoseTTAFold, ESM2, and similar technologies
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