Director, Risk, Data Science and Analytics

Overview

We are seeking a visionary and hands-on Risk Data Science and Analytics Leader to architect the next generation of risk intelligence for a dynamic payments platform serving the property management industry. This role will lead the development of advanced risk models and AI-powered systems across fraud, compliance, and credit domains—while also driving real-time, adaptive decisioning that balances protection with growth. You will spearhead the use of AI agents and real-time risk scoring to automate underwriting and fraud review, while leveraging internal and external data to deliver personalized financial experiences.

These include dynamic transaction and velocity limits, accelerated payment flows for low-risk users, and risk-based upselling and cross-selling strategies. By embedding intelligent risk insights into every customer touchpoint, you will help the business increase approvals, reduce friction, and unlock new revenue opportunities—all while maintaining a secure and compliant ecosystem.

• Design, build, and maintain predictive models for fraud detection, credit risk assessment, and compliance monitoring.
• Develop and maintain enterprise risk scores for tenants and property managers, integrating multi-dimensional signals from compliance, fraud, and credit risk profiles.
• Build real-time fraud risk scoring systems using behavioral analytics, device intelligence, identity verification, and transaction anomaly detection.
• Develop robust forecasting frameworks for credit and fraud losses.
• Deliver accurate and timely reporting to finance, operations, and executive leadership.
• Monitor portfolio performance and identify emerging risk trends, including fraud typologies and attack vectors.
• Lead A/B testing and simulation efforts to evaluate new risk policies and operational strategies.
• Quantify trade-offs between fraud prevention, credit exposure, and customer experience.
• Partner with policy and product teams to iterate on risk rules, thresholds, and fraud mitigation strategies.
• Build models to forecast operational headcount needs based on transaction volumes, risk profiles, and policy changes.
• Develop dashboards and reporting tools to track team productivity and efficiency.
• Support workforce planning and budget allocation with data-driven insights.
• Collaborate with strategic partners, product, and engineering teams to design and implement AI agents to automate manual underwriting and fraud review workflows.
• Leverage natural language processing (NLP), large language models (LLMs), and decision intelligence to streamline document analysis, customer evaluation, and exception handling.
• Build intelligent agents capable of triaging fraud alerts, escalating high-risk cases, and learning from feedback loops.
• Partner with product and engineering to develop dynamic, real-time risk decisioning systems that adapt to user behavior, transaction context, and external signals.
• Integrate internal data (e.g., payment history, behavioral patterns) and external data (e.g., credit bureaus, identity verification, device intelligence) to power customized offerings such as payments acceleration for low-risk users, dynamic transaction and velocity limits based on real-time risk posture, and risk-based pricing, upselling, and cross-selling of financial products.
• Collaborate with product, marketing, and revenue teams to embed risk intelligence into customer journeys and lifecycle strategies.

• Advanced degree in Data Science, Statistics, Economics, Computer Science, or a related field.
• 10+ years of experience in risk analytics, preferably in payments, fintech, or financial services.

• Proven track record of building and deploying risk models and AI solutions in production.
• Strong proficiency in Python, SQL, and machine learning frameworks.
• Experience with fraud detection systems, anomaly detection, and behavioral modeling.
• Hands-on experience with LLMs, NLP, and agentic workflows for operational automation.
• Experience developing composite risk scores using multi-dimensional data sources.
• Strong business acumen and ability to translate risk insights into growth strategies.
• Excellent…