Senior Risk Analyst

SoFi • Frisco, TX

12h • Remote

About The Position

Shape a brighter financial future with us. Together with our members, we’re changing the way people think about and interact with personal finance. We’re a next-generation financial services company and national bank using innovative, mobile-first technology to help our millions of members reach their goals. The industry is going through an unprecedented transformation, and we’re at the forefront. We’re proud to come to work every day knowing that what we do has a direct impact on people’s lives, with our core values guiding us every step of the way. Join us to invest in yourself, your career, and the financial world. Social Finance, LLC seeks Senior Risk Analyst in Frisco, TX:
Job Duties: Design and execute strategies to prevent fraud by leveraging data analysis, industry knowledge, and technological tools such as Python, SQL, Excel, Tableau, and AWS. Analyze patterns and trends related to fraud in the credit card portfolio to identify potential fraudulent activities. Develop mathematical and statistical models to analyze data, interpret results, and effectively communicate findings to both technical and non-technical audiences. Utilize data analytics tools to monitor and detect suspicious behavior, staying ahead of emerging fraud threats and industry trends. Conduct A/B testing to evaluate the effectiveness of fraud prevention strategies and optimize decision-making. Collaborate with risk management, technology, and other cross-functional teams to strengthen fraud prevention efforts, ensuring a comprehensive approach to mitigating fraud risk. Perform in-depth investigations into suspicious activities using data analytics and investigative techniques to recommend appropriate actions. Analyze complex datasets to assess the overall performance of fraud decisioning systems and develop new data-driven rules that balance member experience with fraud prevention. Create and maintain reports on fraud trends, incidents, and mitigation strategies, providing actionable insights to stakeholders and facilitating informed decision-making. Full-time telecommuting is an option.

Requirements

• Master’s degree (or its foreign degree equivalent) in Computer Science, Business Analytics, or a related quantitative discipline, and one (1) year of experience in the job offered or in any occupation in related field.
• Python
• Tableau
• R (Programming language)
• SQL
• MongoDB
• A/B testing
• SAS
• AWS
• Statistics
• Machine learning algorithms
• Java
• Any suitable combination of education, training and/or experience is acceptable.

Responsibilities

• Design and execute strategies to prevent fraud by leveraging data analysis, industry knowledge, and technological tools such as Python, SQL, Excel, Tableau, and AWS.
• Analyze patterns and trends related to fraud in the credit card portfolio to identify potential fraudulent activities.
• Develop mathematical and statistical models to analyze data, interpret results, and effectively communicate findings to both technical and non-technical audiences.
• Utilize data analytics tools to monitor and detect suspicious behavior, staying ahead of emerging fraud threats and industry trends.
• Conduct A/B testing to evaluate the effectiveness of fraud prevention strategies and optimize decision-making.
• Collaborate with risk management, technology, and other cross-functional teams to strengthen fraud prevention efforts, ensuring a comprehensive approach to mitigating fraud risk.
• Perform in-depth investigations into suspicious activities using data analytics and investigative techniques to recommend appropriate actions.
• Analyze complex datasets to assess the overall performance of fraud decisioning systems and develop new data-driven rules that balance member experience with fraud prevention.
• Create and maintain reports on fraud trends, incidents, and mitigation strategies, providing actionable insights to stakeholders and facilitating informed decision-making.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level