Date: Dec 31, 2013
Location: New York, NY, US
Data Scientist - Consumer Decision Sciences-13016207
Description
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’ve launched innovative partnerships with Facebook and Foursquare and aim to build upon our heritage of innovation, adding to the possibilities our network creates for our customers. As a Data Scientist, you will be part of a team dedicated to helping American Express accelerate its digital transformation, and you will be challenged with designing winning applications and developing new Big Data capabilities and innovative that will elevate American Express to the forefront of the digital revolution. Risk and Information Management 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 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.
Job Responsibilities:
- Developing insights into customer behavior and introduce new approaches to transform complex behavioral data into actionable information, such as building predictive models to improve our decisions.
- Recommending the most relevant American Express products, services, or merchant offers which can include explicit Cardmember preferences, location, time, clickstream data, and social media data.
- Intelligently integrating traditional structured data with unstructured data from web and social media.
- Identifying, leveraging, and enhancing statistical learning algorithms relevant for a diverse set of problems, with a focus on understanding data arising from online consumer advertising and engagement.
Qualifications
- You have a passion for empirical research, a desire to work on challenging data problems around the digital advertising ecosystem, and a demonstrated ability to learn and innovate.
- Ability to transform data and prototype quickly to conduct statistical analysis using tools like R, Python, Java, or SAS.
- Proficiency with SQL and relational databases.
- Proficiency with Unix/Linux environments.
- The ability to create a strong network of relationships among peers, internal partners, and external constituencies.
- Strong knowledge of statistical and machine learning techniques, such as regression analysis, clustering, decision trees, collaborative filtering, k-nearest neighbors, association rules, and matrix factorization methods.
- Comfortable communicating with and receiving feedback from both technical and non-technical audiences.
Preferred Additional:
- Familiarity with Hadoop environments and data tools such as Hive or Pig for working with terabyte-scale data.
- Demonstrated ability to apply cutting edge statistical techniques to business problems and to leverage external thinking (from academia and/or industry).
Educational requirement:
- Ph.D. in Mathematics, Physics, Statistics, Economics, Operations Research, Engineering, Computer Science or a related quantitative field.
Job: Risk
Primary Location: US-New York-New York
Schedule: Full-time
Job Segments: Scientific, Database, Scientist, Statistics, Physics, Engineering, Technology, Science, Data
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Description
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’ve launched innovative partnerships with Facebook and Foursquare and aim to build upon our heritage of innovation, adding to the possibilities our network creates for our customers. As a Data Scientist, you will be part of a team dedicated to helping American Express accelerate its digital transformation, and you will be challenged with designing winning applications and developing new Big Data capabilities and innovative that will elevate American Express to the forefront of the digital revolution. Risk and Information Management 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 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.
Job Responsibilities:
- Developing insights into customer behavior and introduce new approaches to transform complex behavioral data into actionable information, such as building predictive models to improve our decisions.
- Recommending the most relevant American Express products, services, or merchant offers which can include explicit Cardmember preferences, location, time, clickstream data, and social media data.
- Intelligently integrating traditional structured data with unstructured data from web and social media.
- Identifying, leveraging, and enhancing statistical learning algorithms relevant for a diverse set of problems, with a focus on understanding data arising from online consumer advertising and engagement.
Qualifications
- You have a passion for empirical research, a desire to work on challenging data problems around the digital advertising ecosystem, and a demonstrated ability to learn and innovate.
- Ability to transform data and prototype quickly to conduct statistical analysis using tools like R, Python, Java, or SAS.
- Proficiency with SQL and relational databases.
- Proficiency with Unix/Linux environments.
- The ability to create a strong network of relationships among peers, internal partners, and external constituencies.
- Strong knowledge of statistical and machine learning techniques, such as regression analysis, clustering, decision trees, collaborative filtering, k-nearest neighbors, association rules, and matrix factorization methods.
- Comfortable communicating with and receiving feedback from both technical and non-technical audiences.
Preferred Additional:
- Familiarity with Hadoop environments and data tools such as Hive or Pig for working with terabyte-scale data.
- Demonstrated ability to apply cutting edge statistical techniques to business problems and to leverage external thinking (from academia and/or industry).
Educational requirement:
- Ph.D. in Mathematics, Physics, Statistics, Economics, Operations Research, Engineering, Computer Science or a related quantitative field.
Job: Risk
Primary Location: US-New York-New York
Schedule: Full-time
Job Segments: Scientific, Database, Scientist, Statistics, Physics, Engineering, Technology, Science, Data
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