Associate Fraud Risk Data

Job Title : Associate Fraud Risk Data

Location : San Jose, CA (Hybrid)

Job Description :

We are looking for a talented, enthusiastic and dedicated person to support the Fraud Risk Data Science Team within the Risk Data & AI Innovation Org. The incumbent will be responsible for supporting key projects associated with fraud detection, risk analysis and loss mitigation. This position requires a person who has experience with machine learning, model development with cutting edge AI / ML frameworks, performing analytics, statistical analysis and model monitoring. Experience with LLMs and other AI tools would be a big plus.

We'd love to chat if you have :

• 2-6 years of experience in machine learning / AI, data science, risk analytics & data analysis within relevant industry experience in eCommerce, online payments, user trust / risk / fraud, or investigation / product abuse.
• Bachelor's / Master's degree in Data Science, Data Analytics, Mathematics, Statistics, Data Mining or related field or equivalent practical experience
• Experience using statistics and data science (machine learning & AI) to solve complex business problems
• Proficiency in SQL, Python, AWS, Excel including key data science libraries
• Proficiency in data visualization including Tableau
• Experience working with large datasets
• Ability to clearly communicate complex results to technical experts, business partners, and executives including development of dashboards and visualizations, ie Tableau.
• Comfortable with ambiguity and yet able to steer AI and machine learning projects toward clear business goals, testable hypotheses, and action-oriented outcomes
• Demonstrated analytical thinking through data-driven decisions, as well as the technical know-how, and ability to work with your team to make a big impact.
• Desirable to have experience or aptitude solving problems related to risk using data science and analytics
• Bonus : Experience with development and implemenation of AI tools (e.g. LLMs) for risk use cases.

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