Associate Fraud Strategy Data Scientist (Hybrid)

Note: This is a hybrid position, so candidates must be in San Jose.

JOB DESCRIPTION:

We are looking for a talented, enthusiastic, and dedicated person to support the Fraud Risk Strategy team. 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 performing analytics, refining risk strategies, and developing predictive algorithms, preferably in the risk domain.

We’d love to chat if you have:

• Maximum 2 years of experience in risk analytics, data analysis, and data science within the relevant industry, with experience in eCommerce, online payments, user trust/risk/fraud, or investigation/product abuse.
• Bachelor’s degree in Data Analytics, Data Science, Mathematics, Statistics, Data Mining, or related field, or equivalent practical experience
• Experience using statistics and data science to solve complex business problems
• Proficiency in SQL, Python, and 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 analytics 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 in solving problems related to risk using data science and analytics
• Bonus: Experience with AWS, knowledge of fraud investigations, payment rule systems, working with ML teams, and fraud typologies

Key Job Functions

• Design rules to detect/mitigate fraud
• Develop Python scripts and models that support strategies
• Investigate novel/large cases
• Identify root cause
• Set a strategy for different risk types
• Work with product/engineering to improve control capabilities
• Develop and present strategies and guide execution

Expected Outcome in 6-12 months

• Work closely with team members and stakeholders to consult, design, develop, and manage fraud strategies and rules that not only solve emerging fraud trends but also provide a great experience to end customers.
• Utilize data analysis to design and implement fraud strategies
• Collaborate with cross-functional stakeholders, including product managers and engineering team,s to deploy data-driven fraud solutions that operate at scale and in real time for end customers.
• Make business recommendations to leadership and cross-functional teams with effective presentations of findings at multiple levels of stakeholders.
• Development of a dashboard and visualizations to track the KPI of fraud strategies implemented

Preferred Skills

• Data analytics and models
• Rule development
• Dashboard Creation
• Project Management
• Strong Communication

MUST HAVE:

• Maximum 2 years of experience in risk analytics, data analysis, and data science within the relevant industry, with experience in eCommerce, online payments, user trust/risk/fraud, or investigation/product abuse.
• Bachelor’s degree in Data Analytics, Data Science, Mathematics, Statistics, Data Mining, or related field, or equivalent practical experience.
• Experience using statistics and data science to solve complex business problems.
• Experience in SQL, Python, and Excel, including key data science libraries.
• Experience applying statistics and data science to tackle intricate business challenges, especially in Fraud mitigation.
• Experience in data visualization, including Tableau.
• Experience working with large datasets.