Role overview

Model Risk Management Lead, Machine Learning

Requirements and responsibilities

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Details

  • Perform independent challenges of machine learning models used for credit underwriting, credit risk, and/or fraud detection through thorough validation and ongoing monitoring
  • 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 MRM framework
  • Partner with Internal Audit, Internal Controls, and Compliance to ensure timely resolution of audit and regulatory requests
  • 4-6 years of professional experience in related technical areas such as model development, model validation, 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 dataset (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 Math, Data Science, Computer Science
  • Oriented toward detail, curious about data/models/algorithms, skilled at critical thinking and problem solving
  • Extraordinary interpersonal and verbal/written communication skills
  • Health care coverage - Affirm covers all premiums for all levels of coverage for you and your dependents
  • Flexible Spending Wallets - generous stipends for spending on Technology, Food, various Lifestyle needs, and family forming expenses
  • Time off - competitive vacation and holiday schedules allowing you to take time off to rest and recharge
  • ESPP - An employee stock purchase plan enabling you to buy shares of Affirm at a discount
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Browse stack
FocusInternal AuditRole area
Seniority signalLeadCandidate level
StackPython, REST, SQLPrimary skills
Location2 accepted countriesEligibility

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