Architect, design, enhance, and build next generation fraud detection solutions. You will support all the product engineering lanes of EOR, with their AI/ML initiatives; Drive the architecture for key cross team/cross product AI/ML projects (via architecture/design documents and developing code key modules); Collaborate with product stakeholders to formulate business problems as technical data-centric problems; Work with software engineers to ensure feasibility of solutions. Deliver prototypes and production code as needed; Experiment with in-house and third-party data sets to test hypotheses on relevance and value of data to business problems
Requirements:
12+ years of relevant work experience with a Bachelor’s Degree or at least 9 years of work experience with an Advanced degree (e.g. Masters, MBA, JD, MD) or 6 years of work experience with a PhD, OR 15+ years of relevant work experience; PhD in Computer Science, Operations Research, Statistics, or highly quantitative field (or equivalent experience) with strength in Deep Learning, Machine Learning, Data Mining, Statistical or other mathematical analysis; Relevant coursework in modeling techniques such as logistic regression, Naïve Bayes, SVM, decision trees, or neural networks
Text:
Principal ML Engineer Architect, design, enhance, and build next generation fraud detection solutions. You will support all the product engineering lanes of EOR, with their AI/ML initiatives; Drive the architecture for key cross team/cross product AI/ML projects (via architecture/design documents and developing code key modules); Collaborate with product stakeholders to formulate business problems as technical data-centric problems; Work with software engineers to ensure feasibility of solutions. Deliver prototypes and production code as needed; Experiment with in-house and third-party data sets to test hypotheses on relevance and value of data to business problems 12+ years of relevant work experience with a Bachelor’s Degree or at least 9 years of work experience with an Advanced degree (e.g. Masters, MBA, JD, MD) or 6 years of work experience with a PhD, OR 15+ years of relevant work experience; PhD in Computer Science, Operations Research, Statistics, or highly quantitative field (or equivalent experience) with strength in Deep Learning, Machine Learning, Data Mining, Statistical or other mathematical analysis; Relevant coursework in modeling techniques such as logistic regression, Naïve Bayes, SVM, decision trees, or neural networks
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