Customer Trust & Partner Support (CTPS) is responsible for creating a trustworthy shopping experience across Amazon stores worldwide by protecting customers, brands, selling partners and Amazon from fraud, counterfeit, and abuse as well as empowering, providing world‐class support, and building loyalty with Amazon’s millions of selling partners. We value individual expression, respect different opinions, and work together to create a culture where each of us is able to contribute fully. Our unique backgrounds and perspectives strengthen our ability to achieve Amazon's mission of being Earth's most customer-centric company.
Amazon’s Account Integrity team within the Customer Trust and Partner Support organization is looking for a passionate, results-oriented Senior Data Scientist to leverage data to drive delivery of projects with huge strategic impact. This team designs and builds high performance systems using machine learning that identify and prevent fraudulent activity and maintain high trust levels with our customers. Fraud prevention is a real-money game where our data science and analytics teams strive to outsmart those who attempt to defraud Amazon and our customers.
As a Senior Data Scientist in the Account Integrity group, you will work directly with ML Scientists, Software Development Engineers and Product Managers to monitor the flavor/trend of fraud worldwide and create appropriate solutions to detect and mitigate fraud in a collaborative environment.
Successful candidates will have broad expertise in a variety of data science disciplines, including both supervised and unsupervised learning methods, strong analytical skills, be detail oriented, and have excellent problem solving abilities. He/she should also have a demonstrated ability to think strategically and analytically about business, product, and technical challenges, with the ability to work cross-functionally.
Roles and Responsibilities:
1. Use statistical and machine learning techniques to create scalable detection systems.
2. Analyze and extract relevant information from large amounts of Amazon's historical business data to help automate and optimize key processes.
3. Design, development and evaluation of highly innovative models for predictive learning.
4. Work closely with software engineering and ML teams to drive real-time model implementations and new feature creations.
5. Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation.
6. Play the role of tech lead on the data science team, mentor fellow scientists.
· Masters in quantitative field (Computer Science, Mathematics, Machine Learning, AI, Statistics, or equivalent).
· Great design and problem-solving skills, passion for quality and engineering excellence at scale.
· At least 4+ years of hands-on experience in applying theoretical models in an applied environment.
· At least 2+ years in at least one scripting language (preferably Python).
· Work well in a fast-moving team environment and effectively deliver technical implementations having complex dependencies and requirements.
· Exemplary communication skills, both verbal and written, ability to work with large cross functional teams of technical and non-technical members.
· Ability to distill informal requirements into problem definitions, dealing with ambiguity and competing objectives.
· A PhD in a quantitative field (Computer Science, Mathematics, Machine Learning, AI, Statistics, or equivalent).
· Expertise in specialized areas such as Deep Learning, Text Mining, and online experimentation etc.
· Extensive knowledge and practical experience in several of the following areas: NLP, deep learning, clustering, graph embedding.
· Skilled with AWS services such as Sage Maker.
· Strong personal interest in learning, researching, and creating new technologies with high commercial impact.