Are you passionate about utilizing data and state-of-the-art AI / ML models to drive impact to one of the fastest growing businesses at Amazon?
How about working with one of the largest fraud prevention systems in the world? Do you enjoy building flexible, performant, and global solutions?
If so, here is a great opportunity to consider!
Amazon Business Payments and Lending (ABPL) is looking for a Sr. Applied Scientist to drive the development of proactive and reactive fraud management strategies across different payment methods while balancing loss rates and customer experience.
They ideal candidates are comfortable working independently in a fast paced, technical, and high energy environment. They are critical thinkers, analytical, innovative, and resourceful, with a history of solving complex and ambiguous problems.
They utilize their deep knowledge of Artificial Intelligence and Machine Learning (AI / ML), their problem solving and analytical skills, and their excellent communication to deliver customer value at-scale.
Key job responsibilities
As a Sr Applied Scientist working in ABPL Fraud Science, your key job responsibilities will include :
1- Extending existing fraud detection scientific techniques, inventing new ones that address customers’ needs or business problems at a product level.
Being the leading author for internal or external publications that validate novelty and are cited by other scientists. Being sought out by scientists as subject matter experts.
2- Partnering with engineering teams to solve complex technical problems. Defining system-level technical requirements, developing implementation plans, guiding adaptation of techniques to meet production requirements, and ensuring consideration of appropriate tradeoffs at the system level.
3- Working tactically and strategically. Delivering end-to-end solutions including scientific contributions. Developing reusable science components and services that resolve architecture deficiencies and customers’ pain points.
Making technical trade-offs for long-term / short- term invention.
4- Taking the lead on medium-to-large business goals. Working on large-scale scientific projects and systems. Delivering significant benefits to customers and business.
About the team
The ABPL Science for Fraud & Personalization team enables best-in-class Science to : 1) develop fraud management strategies that balance loss rates and customer experience across multiple products, 2) target future / current customers with personalized recommendations for B2B financial products.
Our team targets long-term projects with high impact for our customers and for Amazon. We ship models and features incrementally, to iterate quickly and provide value in-flight .
We utilize state of-the-art AI / ML to drive business results with a very large customer base across ABPL. We leverage our ML-ready production environment to increase flexibility and reduce time-to-market.
We are open to hiring candidates to work out of one of the following locations :
Seattle, WA, USA
BASIC QUALIFICATIONS
- 4+ years of building machine learning models for business application experience
- PhD, or Master's degree and 6+ years of applied research experience
- Experience programming in Java, C++, Python or related language
- Excellent communication skills, capable of discussing technical details with a variety of audiences and influencing decision-making processes
PREFERRED QUALIFICATIONS
- Experience with large scale distributed systems such as Hadoop, Spark etc.
- Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
- Expertise in machine learning, statistical analysis, and algorithmic solutions related to fraud and abuse prevention, particularly in Generative AI, Graph-based ML, Behavioral Modeling, and Real-Time Detection
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