Senior Fraud Strategy Analyst

Earnest empowers ambitious professionals to make confident financial decisions and build the life they envision.

What You'll Do:

• Design, develop, and optimize fraud strategies across personal loans and student loan refinance products
• Monitor portfolio performance to identify emerging fraud trends, risks, and optimization opportunities
• Develop and deploy fraud rules and strategies using segmentation, statistical analysis, and predictive modeling techniques
• Own end-to-end fraud mitigation processes, with accountability for fraud loss reduction and performance outcomes
• Design and execute A/B tests and champion/challenger frameworks to evaluate strategy effectiveness
• Build and maintain scalable analytical frameworks using tools such as Snowflake and dbt
• Develop dashboards and reporting (e.g., Looker) to monitor KPIs such as fraud rate, approval rate, and false positives
• Generate actionable insights from structured and unstructured datasets, including limited or noisy data environments
• Partner with cross-functional teams (Product, Engineering, Risk, Operations) and external vendors to implement fraud solutions
• Leverage internal and third-party data to enhance fraud detection capabilities
• Continuously evaluate and refine strategies based on performance metrics and evolving fraud patterns
• Communicate insights, strategy performance, and model behavior clearly to stakeholders and senior leadership
• Support model development lifecycle, including design, validation, and production deployment

About You:

• Bachelor’s degree in a quantitative field (e.g., Statistics, Engineering, Analytics, Economics); Master’s preferred
• 4+ years of experience in fraud risk, credit risk, or customer risk management within a financial institution or fintech
• 3+ years of hands-on experience with SQL, Python, or R for data analysis and modeling
• 3+ Proven experience designing and implementing fraud strategies for unsecured lending products (e.g., personal loans)
• Strong analytical and problem-solving skills with attention to detail
• Ability to translate complex data into clear, actionable insights
• Effective communicator with experience influencing cross-functional stakeholders
• Self-starter with a high degree of ownership, curiosity, and adaptability

Even Better:

• Experience with machine learning models (e.g., logistic regression, tree-based models, anomaly detection)
• Familiarity with fraud typologies (e.g., synthetic identity, first-party fraud, account takeover)
• Knowledge of regulatory frameworks and risk controls (e.g., fair lending, model governance)
• Experience with experimentation frameworks and decision systems
• Exposure to modern data stack tools (e.g., dbt, Snowflake) and BI tools (e.g., Looker)