Director of Fraud and Analytics

The Director of Fraud and Analytics will lead the organization’s fraud detection, pricing optimization, and customer analytics strategy through advanced statistical modeling, machine learning, and artificial intelligence. This role combines expertise in traditional regression-based methods with cutting-edge non-linear and AI-driven approaches to identify fraud patterns, optimize pricing, and deliver actionable insights on customer behavior and value.

1. KEY REQUIREMENTS:
   Minimum experience and requirements:

• Master’s or Ph.D. in Statistics, Data Science, Computer Science, Economics, or related field.
• 10 years of experience in fraud analytics related roles

1. KEY COMPETENCIES:
   You will be responsible for driving the results and development of the organization. To be successful in this role, you must have at least the following key competencies:

• Knowledge of financial services, payments, or e-commerce fraud patterns.
• Experience with customer analytics frameworks (LTV, churn, segmentation).
• Experience deploying models into production environments.

• KEY RESPONSABILITIES:

Strategic Leadership:

• Develop and execute a comprehensive analytics roadmap covering fraud prevention, dynamic pricing, and customer value optimization.
• Collaborate with compliance, finance, marketing, and technology teams to align analytics initiatives with business objectives.

Fraud Analytics & Prevention:

• Build predictive models using traditional regression techniques (e.g., logistic regression, GLMs) for fraud detection.
• Apply machine learning algorithms (e.g., random forests, gradient boosting, neural networks) and non-linear models to uncover complex fraud patterns.
• Partner with engineering teams to enhance fraud prevention capabilities within core applications, ensuring models and rules are integrated effectively for real-time detection.
• Combat identity theft and synthetic identities, bots and automated attacks. Deploy identity protection frameworks leveraging device fingerprinting, biometric validation, and risk scoring.
• Monitor model performance and implement continuous improvements.

Pricing Analytics & AI:

• Develop AI-driven pricing models leveraging demand forecasting, elasticity analysis, and competitive intelligence.
• Use reinforcement learning and optimization algorithms for real-time dynamic pricing strategies.
• Partner with product and finance teams to implement pricing recommendations that balance profitability and market competitiveness.

Customer Analytics:

• Design and maintain models for Customer Lifetime Value (LTV), churn prediction, and segmentation.
• Use advanced analytics to identify high-value customers and optimize retention strategies.
• Integrate behavioral, transactional, and demographic data to drive personalized offers and pricing.

Data Strategy & Governance:

• Oversee data collection, cleansing, and integration from multiple sources (transactional, behavioral, competitive, third-party).
• Ensure compliance with data privacy and security standards.

Reporting & Insights:

• Build dashboards and reporting tools to track fraud metrics, pricing performance, and customer KPIs (LTV, churn, acquisition cost).

Provide actionable insights to senior leadership and recommend proactive measures.

Team Leadership:

• Build and mentor a high-performing analytics team specializing in fraud detection, pricing optimization, and customer analytics.

Foster a culture of innovation and continuous improvement in analytical methodologies.