Design and develop machine learning models to drive impactful decisions; Utilize cutting-edge machine learning approaches and construct sophisticated machine learning models, including deep learning architecture on big data platforms; Work closely with the senior management team to develop ambitious, innovative modeling solutions and deliver them into production; Collaborate with various partners in risk, technology, model governance, etc. throughout the entire modeling lifecycle (development, review, deployment, and use of the models)
Requirements:
Ph.D. or Master’s degree from an accredited university in a quantitative field such as Computer Science, Mathematics, Statistics, Econometrics or Engineering; Hands-on experience in quantitative data management and analytics, including data extraction, cleaning, transformation and visualization; Extensive practical experience and work experience with Machine Learning (supervised and unsupervised). Deep Learning (neutral network) experience is preferred; Experience working with engineering teams to operationalize Machine Learning models
Text:
Risk Machine Learning Modeling (Auto) - Associate Design and develop machine learning models to drive impactful decisions; Utilize cutting-edge machine learning approaches and construct sophisticated machine learning models, including deep learning architecture on big data platforms; Work closely with the senior management team to develop ambitious, innovative modeling solutions and deliver them into production; Collaborate with various partners in risk, technology, model governance, etc. throughout the entire modeling lifecycle (development, review, deployment, and use of the models) Ph.D. or Master’s degree from an accredited university in a quantitative field such as Computer Science, Mathematics, Statistics, Econometrics or Engineering; Hands-on experience in quantitative data management and analytics, including data extraction, cleaning, transformation and visualization; Extensive practical experience and work experience with Machine Learning (supervised and unsupervised). Deep Learning (neutral network) experience is preferred; Experience working with engineering teams to operationalize Machine Learning models
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