
Data Scientist - Global Decision Science
Work with massive amounts of digital data (Web, App, API) and sophisticated tools in an industry leading Big Data environment. Build NLP driven solutions from identifying business problems, to collecting training dataset, to analyzing data, to modeling, to validating and to delivering NLP models. Build everything from basic reports to advanced machine learning models and algos to drive improvements to our customer’s online and mobile app experiences. Work with product owners to revolutionize the product and content design with a data-driven approach. Collaborate with tech partners to test, implement and deploy modeling solutions to production system. Develop insights into customer behavior and introduce new approaches to transform complex behavioral data into actionable information.
Hands-on experience leveraging sophisticated analytical and machine learning techniques
Expertise in an analytical language (Python, R or the equivalent), and experience with databases (Hive, SQL, or the equivalent)
Knowledge of SAS is a plus but not required
Deep understanding of machine learning/statistical algorithms such as deep learning and boosting
Experience with data visualization is a plus
PhD degree with practical experiences NLP is a significant plus.
Sql
Python
SAS
MS PowerPoint
R
Hive
MS Excel skills
Great time management
Driven and self-motivated
Prioritizing skills
Problem-Solving
Flexibility/Adaptability
Detail oriented and highly organized
Multi tasker
Great communication skills
According to JobzMall, the average salary range for a Data Scientist - Global Decision Science in 175 Greenwich St, New York, NY 10007, USA is between $83,700 and $118,000 per year.
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American Express Co. operates as a global services company, which engages in the provision of credit card, financial, and global travel services. It operates through the following business segments: U.S. Card Services, International Card Services, Global Commercial Services, Global Network & Merchant Services, and Corporate.

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