Quantitative Analytics Lead

This is a fully remote position, open to applicants in United States.

📋 Description

• Conduct independent evaluations of machine learning models utilized for credit underwriting, credit risk assessment, and/or fraud detection through rigorous validation and continuous oversight.

• Detect model vulnerabilities and areas for enhancement.

• Work collaboratively with model owners to address model validation issues.

• Collaborate across departments to establish and uphold the company's Model Risk Management (MRM) framework.

• Coordinate with Internal Audit, Internal Controls, and Compliance to ensure prompt resolution of audit and regulatory inquiries.

⛳️ Requirements

• 4-6 years of relevant professional experience in areas such as model development, model validation, and data science.

• Extensive knowledge and experience in machine learning modeling, credit underwriting, credit risk management, and/or fraud detection and fraud risk management.

• Demonstrated ability to work with scripting languages (e.g., Python) and manage large datasets (e.g., SQL).

• Significant experience with machine learning platforms and frameworks (e.g., scikit-learn, pyspark) as well as cloud-based coding environments and databases.

• Bachelor’s, Master’s, or PhD in a quantitative discipline such as Mathematics, Data Science, or Computer Science.

• Detail-oriented, inquisitive about data/models/algorithms, and proficient in critical thinking and problem-solving.

• Exceptional interpersonal and verbal/written communication abilities.

🏝️ Benefits

• Health care coverage - Affirm covers all premiums for all levels of coverage for you and your dependents.

• Flexible Spending Wallets - generous stipends for expenses related to Technology, Food, various Lifestyle needs, and family planning.

• Time off - competitive vacation and holiday schedules that allow you to take time off to rest and recharge.

• ESPP - An employee stock purchase plan that enables you to purchase shares of Affirm at a discounted rate.