[上海张江] eBay Applied Researcher (Risk)

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About The Team

At eBay, you will be part of a purpose driven community dedicated to creating a bold and versatile work environment. In eBay Payments, you will be an integral member of a growing organization that inspires passion, courage and inventiveness - creating the future of global commerce and making an important, positive impact on millions of eBay sellers and shoppers around the world. If you are looking for a special place to take your Payments career to the next level, we want to talk with you!

Risk Management is at the core of Payments done well and we are hiring curious, driven, and courageous experts to transform our business unit to enable eBay's next generation Payments strategy. Our focus is to ensure the integrity of our marketplace for buyers and sellers who transact with us every single day. The scope of our charter includes Risk Management Strategy, Policy, Decision Management, and Policy Operations.

We are looking for a highly talented and self-motivated applied researcher to join our Decision Science team. Decision Science contains both applied researchers and software engineers responsible for creating and implementing state of the art machine learning algorithms for fraud detection and risk assessment in support of Risk Management. .

About The Role

The primary responsibility of this role is to build and deploy AI algorithms inside of a high throughput, low latency and customer facing environment. The applied researcher will support the risk department, leveraging big data technologies to aggregate and structure data, perform statistical analysis, and build algorithmic solutions to reduce fraud, monitor our buyers and sellers, and intermediate payments to improve the overall eBay experience. As a member of the decision science team, you will research and develop new methodologies and techniques to improve the overall effectiveness of risk management. Mine and analyze massive amounts of unique internal and external data to gain deep business knowledge and insight on customer activity and usage behaviors and their relationships with fraud, credit risks, and other types of behaviors. The ideal candidate is a blend of science and engineering skills with capabilities stretching from problem statement to algorithm deployment.

Responsibilities:

• Work with business partners, engineers, and multi-functional teams to produce end-to-end production-ready solutions.
• Analyze Big Data to produce actionable insights
• Provide technical leadership and mentoring to other team members
• Translate business problems into Machine Learning problems
• Build quick prototypes and preliminary explorations driving next generation model iterations
• Document and publish results and research to internal and external audiences

Required Skills and Experience:

• MS/PHD preferred, 2+ years experience
• Strong knowledge of 1 or more scripting and programming languages (Python, Java, Scala, etc.)
• Experience in SQL, NoSQL databases
• Experience with Big Data technology: Hadoop framework: Hive, Spark, etc. a plus
• Expertise in machine learning, deep learning technologies using Python, Tensorflow, PyTorch
• Background in a variety modeling techniques: GBM, logistic regression, clustering, neural networks, NLP
• Strong analytical skills with good problem solving ability