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Amazon Applied Scientist 
United States, New Jersey, Newark 
942587682

20.11.2024
DESCRIPTION

ABOUT THIS ROLE
In this role, you'll employ scalable cutting-edge machine learning (ML), deep learning (DL), and Natural Language Processing (NLP) techniques to detect and predict fraudulent activities, enhance fraud investigation capabilities, and develop advanced fraud protection and defense mechanisms. You'll leverage these technologies to analyze complex patterns in transaction data, identify anomalies, and create predictive models that can anticipate potential fraud before it occurs. Your work will be crucial in safeguarding the company's assets, protecting customers from financial harm, and maintaining the integrity of our systems. You'll translate intricate fraud patterns into actionable insights, enabling rapid response to emerging threats and informing critical business decisions related to risk management. You'll operate in an agile environment in which we own and collaborate on the life cycle of research, design, and model development of relevant projects.As an Applied Scientist, you will...
ABOUT AUDIBLE

BASIC QUALIFICATIONS

- MS in one of the following disciplines: Computer Science, Statistics, Data Science, Economics, Applied Math, Operational Research or a related quantitative field +5 yrs relevant experience; or PhD
- Fluency in Python, SQL or similar scripting languages and skilled at Java, C++, or other programing languages
- Experience in algorithm development
- Depth and breadth in state-of-the-art machine learning technologies
- Machine Learning Pipeline orchestration with AWS (SageMaker, Batch, Lambda, Step Functions) or similar cloud-platforms
- Big Data Engineering with Spark / AWS EMR & Glue


PREFERRED QUALIFICATIONS

- Domain knowledge of comparable products (digital, retail)
- Publications at top-tier peer-reviewed conferences or journals in one of those areas (natural language processing/understanding, deep learning, machine learning, or speech processing)
- Proven track record of innovation in creating novel algorithms and advancing the state of the art