Leadership in modeling, technology, technical product development/design, and technical product ownership; Mentor junior team members; Refine and develop team best practices; Develop and deliver presentations to communicate technical ideas and analytical findings to non-technical partners and senior leadership, including underwriters and IT professionals; Build underlying software infrastructure to better manage, integrate and mine the data that LabCorp processes daily; Work closely with engineering teams and with some supervision participate in the full development cycle from product inception, research and prototyping to release in production
Requirements:
Advance degree is in Computer Science, Engineering, Statistics, Math or related field; Experience in artificial intelligence and statistical learning; Experience with statistical methodologies and machine learning techniques such as: neural networks, graphical models, ensemble methods and natural language processing; Experience with multiple deep learning techniques such as CNN, LSTM, RNN, etc., in addition to standard machine learning approaches such as those found in scikit-learn
Text:
Principal Data Scientist Leadership in modeling, technology, technical product development/design, and technical product ownership; Mentor junior team members; Refine and develop team best practices; Develop and deliver presentations to communicate technical ideas and analytical findings to non-technical partners and senior leadership, including underwriters and IT professionals; Build underlying software infrastructure to better manage, integrate and mine the data that LabCorp processes daily; Work closely with engineering teams and with some supervision participate in the full development cycle from product inception, research and prototyping to release in production Advance degree is in Computer Science, Engineering, Statistics, Math or related field; Experience in artificial intelligence and statistical learning; Experience with statistical methodologies and machine learning techniques such as: neural networks, graphical models, ensemble methods and natural language processing; Experience with multiple deep learning techniques such as CNN, LSTM, RNN, etc., in addition to standard machine learning approaches such as those found in scikit-learn
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