The ‘Product Abuse team’ is part of the Global Customer Analytics and Data Science team within Enterprise Digital and Analytics (EDA) group in American Express. This team is responsible for developing data products and machine learning capabilities that identify and prevent abuse risk across various products and services.
The incumbent will collaborate with a high performing team responsible of managing the end to end Product Abuse Prevention Strategy including:
- Analyze large volumes of data using advanced statistical techniques to uncover the emerging trends in customer abuse risk patterns on various products and services.
- Design, develop, enhance and implement business rules, strategies and capabilities to control losses.
- Develop predictive models utilizing latest machine learning techniques to control and minimize abuse risk across various products and services.
- Identify and evaluate new data sources. Employ feature engineering to discover new predictive features from new data sources and thus adding incremental value to the models and processes.
- Leverage advanced data visualization tools to discover complex abuse patterns.
- Develop actionable data-driven insights through a deep understanding of the business and its data.
- Monitor the impact of controls on customers and develop strategies to ensure a positive customer experience.
- Deliver effective presentations and making recommendations to both senior leadership and working partners.
- Collaborate with wide variety of partners such as Risk, Marketing, Legal and compliance to derive strategic insights, provide thought leadership into key findings and actionable recommendations to influence business strategy
- Excellent analytical and quantitative skills.
- Hands-on experience managing/ leading projects using large volumes of data and application of advanced predictive analytics
- Proficiency in SQL, Hive, Spark is strongly preferred.
- Proficiency in Python (and/or) R is strongly preferred.
- Extensive knowledge and practical experience on machine learning techniques including regression & classification methods, boosting methods (GBM, XGBOOST), and neural networks (ANN).
- Proactive and able to develop creative and innovative solutions
- Strong interpersonal skills with a history of maintaining good working relationships with business and technical partners
- Must be results-oriented, able to manage multiple initiatives, and communicate effectively.
- Strong experience managing multiple projects concurrently in changing deadlines.
Master’s degree in Mathematics, Computer Science, Statistics, Economics, or related quantitative field is desired
Bachelor's degree required.
Schedule (Full-Time/Part-Time): Full-time
Date Posted: Sep 25, 2019, 7:11:59 PM