Job Description

At eBay, we're more than a global ecommerce leader — we’re changing the way the world shops and sells. Our platform empowers millions of buyers and sellers in more than 190 markets around the world. We’re committed to pushing boundaries and leaving our mark as we reinvent the future of ecommerce for enthusiasts.

Our customers are our compass, authenticity thrives, bold ideas are welcome, and everyone can bring their unique selves to work — every day. We're in this together, sustaining the future of our customers, our company, and our planet.

Join a team of passionate thinkers, innovators, and dreamers — and help us connect people and build communities to create economic opportunity for all.

About the team and the role:

You will join eBay Risk, a purpose-driven team focused on marketplace integrity for the buyers and sellers who transact on eBay every day. The charter spans Risk Management Strategy, Policy, Risk Science, and Policy Operations. Risk Science brings applied researchers and software engineers together to create and implement state-of-the-art machine learning for fraud detection and risk assessment.

This role develops algorithms in a high-throughput, low-latency, big data environment and researches methods that improve the effectiveness of risk management. The work supports the risk organization by aggregating and structuring data, running statistical analysis, and building models that reduce fraud, monitor buyers and sellers, and intermediate payments. Problem sets are focused on fraud and risk management: develop and train models to prevent fraudsters from listing, monetizing, and violating platform policies.

What you will accomplish:

• 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.
• Act as the technical owner of projects that may require significant customization of existing analytic tools, techniques, and processes, or development of new ones.
• Perform statistical data analysis, ensure data quality, and develop tracking and reporting systems to determine the effectiveness of models, rules, and other risk initiatives and programs.
• Design and create systems to structure, aggregate, and turn petabytes of messy information into statistically significant features for modeling purposes.
• Work with Business Unit Partners and Policy Makers to scope and formulate real-world problems into highly scalable and repeatable solutions.
• Partner with the Data Engineering team to deploy models to the production environment.

What you will bring:

• Master’s degree in a quantitative field: computer science, math, statistics, etc.
• Two years of working experience in Deep Learning (NLP, CV) deployed in high-volume, low-latency, user-facing applications
• Experience with big data technology and the Hadoop ecosystem: Hive, Spark, etc.
• Expertise in machine learning packages: Python, R, etc. Strong knowledge of one or more scripting and programming languages (Python, Java, Scala, etc.)
• Background in a variety of modeling techniques: GBM, logistic regression, clustering, NLP
• Strong analytical skills with good problem-solving ability. Good presentation and communication skills.

Additional Details

eBay is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, sex, sexual orientation, gender identity, veteran status, and disability, or other legally protected status. If you have a need that requires accommodation, please contact us at talent@ebay.com. We will make every effort to respond to your request for accommodation as soon as possible. View our accessibility statement to learn more about eBay's commitment to ensuring digital accessibility for people with disabilities.

We use cookies to enhance your experience and may use AI tools for administrative tasks in the hiring process. To learn how we handle your personal data and use AI responsibly, please visit our Talent Privacy Notice, Privacy Center, and AI Hiring Guidelines.