Functional Description
Decision Science colleagues will serve as a key member of the Credit and Fraud Risk organization. We seek a thought-leader and a problem-solver who can blend business, technical, and industry best practices when it comes to developing the analyses, models, and algorithms that power our customers’ digital experiences.
This critical team is responsible for managing enterprise risks throughout the customer lifecycle, across our consumer and commercial businesses, and across all our global products. We develop industry-first data capabilities, build profitable decision-making frameworks, create machine learning-powered predictive models, and improve customer servicing strategies.
Our Decision Science teams use industry leading modeling and AI practices to predict customer behavior. We develop, deploy and validate predictive models and support the use of models in economic logic to enable profitable decisions across credit, fraud, marketing and servicing optimization engines.
Responsibilities
- Work with massive amounts of digital data (Web, App, API) and sophisticated tools in an industry leading Big Data environment.
- Build everything from basic reports to advanced machine learning models and algos to drive improvements to the customer’s online and mobile app experiences.
- Work with product owners to revolutionize the product and content design with a data-driven approach.
- Collaborate with tech partners to test, implement and deploy modeling solutions to production system.
- Develop insights into customer behavior and introduce new approaches to transform complex behavioral data into actionable information.
- Leverage the power of closed loop through Amex network to make decisions more intelligent and relevant.
- Innovate with a focus on developing newer and better approaches using big data & machine learning solutions.
Qualifications
- PhDs in a quantitative field (Computer Science, Statistics, Mathematics, Physics, Operation Research and etc.) with hands-on experience leveraging sophisticated analytical and machine learning techniques. PhD degree with practical experiences NLP is a significant plus.
- Expertise in an analytical language (Python, R or the equivalent), and experience with databases (Hive, SQL, or the equivalent). Knowledge of SAS is a plus but not required.
- Deep understanding of machine learning/statistical algorithms such as deep learning and boosting. Experience with data visualization is a plus.
- Demonstrated ability to frame business problems into mathematical programming problems, leverage external thinking and tools (from academia and/or other industries) to engineer a solution and deliver business insights.
- Ability to work effectively in a team environment.
- Independent thinker who’s organized, has great attention to detail, and can multi-task.
- Strong communication skills.
- Ability to learn quickly and work independently with sophisticated, unstructured initiatives.
- Ability to integrate with cross-functional business partners worldwide.
- Proficient in presentation tools, including Excel and PowerPoint.