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Analytics Manager
American Express
Phoenix, AZ, United States
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This Job Posting is for Grace Hopper Conference
About American Express
American Express is a global services company that provides consumers and businesses with exceptional access to products, insights and experiences that enrich lives and build business success. We make it easier, safer and more rewarding for consumers and businesses to purchase the things they need and for merchants to sell their goods and services through innovative payment, travel and expense management solutions.
Our direct relationships with millions of consumers, businesses and merchants worldwide -- combined with our leading-edge marketing, analytical, rewards, and servicing capabilities -- enable us to uniquely offer an array of differentiated experiences that enrich lives. American Express was built on service, sustained by innovation and our vision is to work hard every single day to provide the world’s best customer experience every day.
About Credit and Fraud Risk
Purposeful, ambitious, insatiable, analytical, collaborative and courageous are the hallmarks of our Credit and Fraud Risk team.
This critical team is responsible for managing enterprise risks through the customer life cycle and across all our global products. We develop industry-first data capabilities, build profitable decisioning frameworks, create machine-learning powered predictive models, and improve customer servicing strategies. This team has delivered industry-leading results year after year while enabling profitable growth and delivering a best-in-class customer experience.
This team is comprised of several diverse teams structured under four, core functions:
• Credit Risk Strategy : Monitor portfolios and make profit-based risk decisions at all stages of the customer credit life cycle
• Decision Science : Build predictive models using AI/Machine Learning for all portfolios and products
• Fraud Risk Strategy : Develop fraud prevention strategies on behalf of our customers
• Risk Capabilities : Develop capabilities to manage data and execute business strategies
Job Responsibilities
You will be responsible for developing and maintaining predictive models using advanced statistical and Machine Learning techniques like Gradient Boosting Machine, XGBoost, look alike modeling, etc. You will partner with cross-functional teams like underwriting, finance and marketing, provide decision science support and enhance credit risk decisions.
Qualifications
Qualifications
• Advanced Degree (i.e. MBA or MS, PhD) in a quantitative field such as Economics, Statistics, Mathematics, Operations Research, Engineering, Computer Science
• Advanced degree is preferred but not required given a suitable combination of formal education and experience
• Strong analytical and problem solving skills
• Strong working knowledge of statistical and data mining techniques, including regression analysis and Machine Learning tools for pattern recognition and predictive analytics
• Experience working with very large datasets using Big Data tools and platforms (Hadoop, PIG/HIVE/Mahout) is preferred
• Hands-on knowledge of statistical/mathematical software (e.g. SAS, R, Python) and database query languages (e.g. SQL)
• Thought leadership and solution oriented mindset
• Ability to think strategically and apply both quantitative methods and business insight to get results
• Ability to build strong relationships in a cross-functional environment
• Clear, effective written and oral communication skills
Depending on factors such as business unit requirements, the nature of the position, cost and applicable laws, American Express may provide visa sponsorship for certain positions.
Creating an inclusive culture is both a part of who we are and what we do together. We value and embrace the diversity of thought, backgrounds and experiences of all our employees. Through our 15 Employee Networks, participants can broaden their associations with other employees while expanding their knowledge of the business. American Express is a place where being yourself matters. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
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