American Express Careers
Data Scientist and Machine Learning Researcher
American Express invites INFORMS Conference attendees to share your resume so you can be considered for future Data Scientist and Machine Learning Researcher opportunities in the New York based Decision Science Team.
American Express is working on our company’s next transformation - integrating into the digital universe and developing new forms of payment and lifestyle services. We have launched innovative partnerships with top technology companies and aim to build upon our heritage of innovation, adding to the possibilities our network creates for our customers. As a Data Scientist in the Machine Learning and Data Science Team, you will help American Express accelerate its digital transformation. You will be challenged with designing winning data products and developing new big data capabilities that will elevate American Express to the forefront of the digital revolution.
Credit and Fraud Risk department comprises several teams which manage the Company's credit, market, and operational risk, with work extending across the customer life cycle, from identifying profitable prospective customers, defining approval and underwriting criteria, to determining efficient cross-sell methods and setting strategies for collections. Teams are strategically focused on building global information platforms, transforming the way we market to customers and providing robust analytics to develop new digital partnerships and enhance our ecommerce capabilities.
- Ph.D. degree in Computer Science, Statistics, Operations Research, Engineering, Mathematics, Economics, Physics, or a related quantitative field is required for this position;
- Expert knowledge of machine learning and statistical modeling methods for supervised and unsupervised learning, understanding of underlying algorithms, ability to tune and analyze algorithms and extract actionable insights. These methods include (but not limited to) regression analysis, clustering, outlier detection, novelty detection, decision trees, collaborative filtering, nearest neighbors, support vector machines, ensemble methods and boosting, neural networks and deep learning, feature selection, and factorization methods;
- Ability to work with raw data (including big data), knowledge of preprocessing procedures and ability to prepare data for modeling; experience in feature engineering;
- Proven track record of working on different Machine Learning projects; high positions in Kaggle and other machine learning competitions are preferred;
- Exceptional programming skills in Python and Scala. Additional knowledge of C/C++ and Java is preferred;
- Experience with distributed computing environments (including YARN, Spark and Hadoop) and cloud computing platforms (Amazon Web Services/AWS, Google Cloud, Microsoft Azure)
- Strong knowledge of databases and related languages/tools such as SQL, NoSQL, Hive, etc;
- Ability to work in a dynamic, cross-functional environment, with a strong attention to detail;
- Effective communication skills and ability to explain complex data products in simple terms;
- Strong relationship building and collaborative 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.
There’s a difference between having a job and making a difference.
American Express has been making a difference in people’s lives for over 160 years, backing them in moments big and small, granting access, tools, and resources to take on their biggest challenges and reap the greatest rewards.
We’ve also made a difference in the lives of our people, providing a culture of learning and collaboration, and helping them with what they need to succeed and thrive. We have their backs as they grow their skills, conquer new challenges, or even take time to spend with their family or community. And when they’re ready to take on a new career path, we’re right there with them, giving them the guidance and momentum into the best future they envision.
Because we believe that the best way to back our customers is to back our people.
Schedule (Full-Time/Part-Time): Full-time
Date Posted: Nov 1, 2018, 8:24:33 AM