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The Strategy & Development Analyst II at Affirm will guide high priority go-to-market initiatives, managing project rollouts and ensuring GTM readiness. Responsibilities include strategic decision-making, maintaining execution playbooks, and collaborating closely with stakeholders to monitor progress and address feedback.

The Analyst I, Fraud Strategy and Analytics will support fraud monitoring, detection, and mitigation efforts, research emerging fraud trends, and help enhance fraud detection systems through data analysis and collaboration with cross-functional teams.

As a Software Engineer II at Affirm, you'll develop features for the loans platform, enhance design and code quality, collaborate across teams, and support product growth and scalability.

The Director of IT Engineering at Affirm will lead Asset Management, Logistics, and Client Engineering functions. Responsibilities include defining ITSM strategies, implementing asset management platforms, establishing IT governance, and enhancing security postures while partnering with various teams to support internal customers effectively.

The Business Development Associate at Affirm drives new business by identifying, qualifying, and developing new partnership opportunities. They collaborate with Account Executives to strategize outreach campaigns, manage the sales pipeline, and engage potential partners. The role requires a Bachelor's Degree or equivalent experience, 6 months to 1 year of prior work experience in outbound sales, and a strong passion for E-commerce.

Manage third-party risk management activities, develop risk mitigation plans, establish risk reporting, collaborate with stakeholders, enhance program policies, and support broader risk management programs. Requires 4-6 years of experience in third-party risk or enterprise risk in a regulated environment.

The Analyst II, Fraud Strategy at Affirm works cross-functionally to combat fraudulent activities and enhance fraud prevention strategies. Responsibilities include identifying opportunities to improve fraud prevention, translating analysis into decisioning logic, monitoring system performance, developing frameworks for fraud machine learning models, and evaluating external data sources for risk management. The ideal candidate has 2-5 years of experience in data analytics or data science in fraud/identity or credit risk, strong technical skills in SQL, Python, and R, and the ability to collaborate across teams. A Bachelor's or Master's degree in a quantitatively rigorous discipline is required.