Quantitative Analytics Lead

About the Role

Your day-to-day responsibilities will involve performing independent challenges of machine learning models used for credit underwriting, credit risk, and fraud detection, as well as collaborating with stakeholders to implement and maintain the company's model risk management framework. You will have the opportunity to work with a range of technical tools and platforms, including Python, SQL, and cloud-based coding environments.

The Quantitative Analytics team at Affirm is part of the Enterprise Risk and Internal Audit department, which is responsible for protecting the company's operations and long-term success. As a member of this team, you will be expected to uphold the highest standards of accountability, transparency, and resilience, and to contribute to the development of a culture that prioritizes responsible growth and risk management.

What You Will Do

• Perform independent challenges of machine learning models used for credit underwriting, credit risk, and fraud detection
• Identify model weaknesses and opportunities for improvement
• Collaborate with model owners to remediate model validation findings
• Partner cross-functionally to implement and maintain the company's model risk management framework
• Partner with Internal Audit, Internal Controls, and Compliance to ensure timely resolution of audit and regulatory requests
• Develop and maintain documentation of model validation procedures and results
• Stay up-to-date with industry trends and developments in model risk management and machine learning
• Collaborate with data scientists and engineers to design and implement new models and algorithms
• Develop and maintain dashboards and reports to track model performance and risk metrics

What We Are Looking For

• 4-6 years of professional experience in related technical areas, such as model development, model validation, or data science
• Deep and broad knowledge and experience in machine learning modeling, credit underwriting, credit risk management, and/or fraud detection and fraud risk management
• Proven ability to work with script languages (e.g., Python) and large-scale datasets (e.g., SQL)
• Extensive experience with machine learning platforms and frameworks (e.g., scikit-learn, PySpark) and cloud-based coding environments and databases
• BS, MS, or PhD in a quantitative field, such as mathematics, data science, or computer science
• Oriented toward detail, curious about data/models/algorithms, and skilled at critical thinking and problem-solving
• Extraordinary interpersonal and verbal/written communication skills

Nice to Have

• Experience with cloud-based data platforms, such as AWS or GCP
• Knowledge of regulatory requirements and industry standards for model risk management
• Familiarity with agile development methodologies and version control systems, such as Git
• Experience with data visualization tools, such as Tableau or Power BI

Benefits and Perks

• Competitive salary range (
  64,000 - $245,000 per year, depending on location and experience)
• Equity rewards and stock options
• Monthly stipends for health, wellness, and tech spending
• 100% subsidized medical coverage, dental, and vision for you and your dependents
• Flexible PTO and remote work arrangements
• Opportunities for professional development and career growth
• Access to cutting-edge technologies and tools
• Collaborative and dynamic work environment

• Be prepared to showcase your technical skills in Python, SQL, and cloud-based coding environments, and to explain complex data insights to non-technical stakeholders.
• Highlight your experience with machine learning platforms and frameworks, such as scikit-learn and PySpark, and your ability to work with large-scale datasets.
• Emphasize your attention to detail and your ability to identify and mitigate model risk, as well as your experience with model validation and remediation.
• Showcase your ability to communicate complex technical concepts to cross-functional teams, and to collaborate with stakeholders to implement and maintain model risk management frameworks.
• Be prepared to discuss your experience with data visualization tools, such as Tableau or Power BI, and your ability to develop and maintain dashboards and reports to track model performance and risk metrics.
• Research the company's culture and values, and be prepared to discuss how your skills and experience align with those values.
• Prepare to discuss your experience with agile development methodologies and version control systems, such as Git, and your ability to work in a fast-paced and dynamic environment.