More than 70%+ of the time spent on coding and/or hands-on technical implementation of re-usable frameworks to drive adoption of Machine Learning in Amex, Combine Data Science and engineering skills to solve very interesting problems that directly and greatly impact millions of individuals worldwide, Not only creating ML model prototypes but also putting them into production in scale, Challenge the way we do business through the application of technology and machine learning solutions to integrate insights in the operational workflow, Be part of a dynamic team and play a visible role in implementing cutting-edge technology and machine learning solutions with other industry-leading partners, Works with large, complex data sets to build products and tools that enhance colleague and customer experiences, Implement highly scalable platform components and tools leveraging machine learning and deep learning models to solve real-world problems in areas such as Speech Recognition, Natural Language Processing, Best Next Action, and Time Series predictions, You will be part of a Data Science / Machine Learning team to design, code, train, test, deploy and iterate on large scale machine learning systems, You will work with Engineering, Analytics, and Data Science experts to strive for greater functionality in the technology ecosystem, You will help craft the direction of machine learning and artificial intelligence at AmEx, Research on enhancing existing Machine Learning and NLP algorithms for decision science applications, and explore new techniques for solving specific business problems, Explore new Consumer/Commercial unstructured data sources to enhance credit decision science across portfolios, Create new Data Products/new insights from Data science and establish regular communication with decision science leaders, Use techniques from artificial intelligence/machine learning/deep learning to solve supervised and unsupervised learning problems, Partner with modeling teams to create new/enhance existing variables through regular case reviews. .