Amex
Gurgaon
Financial Transaction Processing
You Lead the Way. We’ve Got Your Back.
At American Express, we know that with the right backing, people and businesses have the power to progress in incredible ways. Whether we’re supporting our customers’ financial confidence to move ahead, taking commerce to new heights, or encouraging people to explore the world, our colleagues are constantly redefining what’s possible — and we’re proud to back each other every step of the way. When you join #TeamAmex, you become part of a diverse community of over 60,000 colleagues, all with a common goal to deliver an exceptional customer experience every day. We back our colleagues with the support they need to thrive, professionally and personally. That’s why we have Amex Flex, our enterprise working model that provides greater flexibility to colleagues while ensuring we preserve the important aspects of our unique in-person culture. Depending on role and business needs, colleagues will either work onsite, in a hybrid model (combination of in-office and virtual days) or fully virtually.
BU & LOB Description:
The CFR team helps drive profitable business growth by reducing the risk of fraud and maintaining our customers’ confidence in the security of our products. It utilizes an array of tools and ever-evolving technology to detect and combat fraud, minimize the disruption of good spending and provide a world-class customer experience. The team leads efforts that leverage data and digital advancements to improve service and risk management as well as enable commerce and drive innovation. CFR is responsible for developing and monitoring statistical models for predicting individual and commercial’ behaviors such as credit risk, fraud risk, spending and revolve. These models are used for key business decisions made across the customer life cycle to manage risk and accelerate profitable business growth. Underpinning our growth as a company are the tools and capabilities that ensure we prudently take and manage risk in a viable way.
Roles & Responsibilities:
The selected candidate will be part of the Global Non Card Lending Decision Science Team based out of Bangalore and will be part of the team developing predictive models and evaluating new data sources.
- Build and deploy the next generation of credit risk management models for NCL and capabilities to deliver industry-leading credit quality and to enable accelerated profitable growth.
- The candidate will also have to work closely with partners in the risk strategy teams to drive innovations in risk decisions through development of best in class solutions.
- The candidate will drive the next generation of credit risk models by leveraging big data, machine learning algorithms and enhanced variable & data intelligence
- 0-3 years with relevant experience in Analytical/Modelling Skills
- Post Graduate in Statistics/Mathematics/Economics/ Engineering/Management
- Data Science/Machine Learning/Artificial Intelligence
- Should possess strong analytical and problem solving skills and a tremendous will to win
- Data Science/Machine Learning/Artificial Intelligence
- Expertise in Coding, Algorithm, High Performance Computing
- Unsupervised and supervised techniques – : active learning, transfer learning, neural models, Decision trees, reinforcement learning, graphical models, Gaussian processes, Bayesian models, Map Reduce techniques, attribute engineering
- Deep learning
- Gradient boosting machines, self-reinforcing algorithms
- Technical Skills/Capabilities:
- Analytics & Insights & Targeting o R, Python, C, C++, Java, SAS SQL
- Advanced Statistical Techniques
- Data correlation
- Model Accuracy Techniques : Gini, Concordance, F-Score
- Knowledge of Platforms: Hadoop, Big Data – Cornerstone
American Express is an equal opportunity employer and makes employment decisions without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability status, age, or any other status protected by law.
Offer of employment with American Express is conditioned upon the successful completion of a background verification check, subject to applicable laws and regulations.
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