Amount

Director of Fraud, Risk, and Data Science

Amount

full-time

Posted on:

Origin:  • 🇺🇸 United States

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Salary

💰 $165,000 - $192,500 per year

Job Level

Lead

Tech Stack

AirflowApacheASP.NETAWSAzureCloudEntity FrameworkGoogle Cloud PlatformHadoopInformaticaJavaJavaScriptKubernetesMS SQL Server.NETNode.jsPandasPythonRubyScalaSparkSQL

About the role

  • Take ownership of model development, selection, and optimization with a focus on fraud and risk
  • Oversee, maintain, and evolve in-house models using machine learning techniques
  • Strong product bias with significant client-facing component to maximize product performance
  • Model Development, Management & Evolution: monitor performance, identify improvements, implement enhancements with advanced ML algorithms
  • Fraud Prevention: lead evolution of core fraud prevention capabilities, conduct build vs. buy analyses, assess third-party fraud models
  • Model Governance: ensure models are governed, respond to model validation requests, explain functionality, provide performance and compliance evidence
  • Policy Optimizer Analysis: leverage statistical methods to help clients configure credit policies and optimize underwriting rules
  • Customer Success: work with customers to evolve and maximize credit and fraud policies via regular meetings and insights
  • Cross-Functional Collaboration: partner with Product, Engineering, and Customer Success to integrate and optimize models
  • Data-Driven Insights: analyze large datasets to uncover trends, identify risks, and find opportunities for product innovation

Requirements

  • 7+ years of professional experience in a data science role with emphasis on credit and/or fraud risk management within financial services or fintech
  • Bachelor's degree in Data Science, Statistics, Mathematics, Computer Science, or related field
  • Proficiency in Python and SQL for data manipulation, modeling, and analysis
  • Hands-on experience developing, validating, and implementing machine learning models (e.g., Logistic Regression, Gradient Boosting, Random Forest, Neural Networks)
  • Familiarity with decision tree analysis and its application in a business context
  • Deep understanding of statistical concepts and ability to translate data-driven insights into actionable recommendations
  • Excellent verbal and written communication skills for technical and non-technical audiences, including clients
  • Strategic problem solver comfortable with ambiguity
  • Nice to have: Experience with large-scale datasets and cloud platforms (AWS, GCP, Azure)
  • Nice to have: Familiarity with model validation best practices and regulatory requirements
  • Nice to have: Previous client-facing or consulting experience