Fraud Data Analyst

Overview

The Fraud Strategy Analyst is responsible for supporting the development, testing, and ongoing optimization of fraud strategies, policies, rules, thresholds, and decision logic across RealPage’s payments ecosystem. This role will focus on fraud prevention and detection across new account onboarding, tenant payments, vendor payments, owner draws, funding instruments, limit management, and payout activity.

This is a hands-on, technical role for a fraud professional with strong analytical skills, data science exposure, and mandatory SQL experience. The ideal candidate can query data independently, identify fraud patterns, test hypotheses, evaluate strategy performance, and translate findings into practical fraud controls while balancing risk mitigation, customer experience, operational workload, and business growth.
Responsibilities
Fraud Strategy, Policy & Controls

- Support development and maintenance of fraud risk policies, strategies, rules, thresholds, decision logic, treatment paths, and control documentation across onboarding and monitoring workflows.

- Help build and optimize controls for payment fraud, onboarding risk, account takeover, business email compromise, counterparty fraud, tenant payment fraud, synthetic identity, first-party misuse, bust-out behavior, stolen payment instruments, and emerging typologies.

- Document strategy rationale, rule logic, expected impact, monitoring plans, policy considerations, change history, and recommended follow-up actions Analytics, Data Science & Rule Performance

- Use SQL to independently query data, validate hypotheses, identify fraud patterns, assess false positives, and evaluate loss exposure, operational impact, and customer friction.

- Apply analytical and data science methods to support feature exploration, segmentation, model output evaluation, threshold setting, experimentation, champion/challenger comparisons, and performance monitoring.

- Partner with Risk Data Science & Analytics to translate dashboards, models, features, risk scores, and analytical insights into practical fraud decision strategies and operational controls.

Operational Feedback & Cross-Functional Execution

- Partner with Onboarding Risk Operations and Risk Monitoring Operations to incorporate case outcomes, queue trends, investigator feedback, alert quality, and operational pain points into strategy improvements.

- Review themes from Trust and Safety escalations to identify control gaps, recurring fraud signals, product or process vulnerabilities, or policy needs requiring durable remediation.

- Collaborate with Product, Engineering, Payment Operations, Compliance/AML, Legal, and Operational Excellence on tooling, workflow, data availability, rule implementation, and control monitoring.

- Provide concise updates on fraud trends, strategy performance, emerging risks, rule effectiveness, false positive impact, and recommended actions to fraud leadership and cross-functional stakeholders.

Qualifications
Required:

- 3-5 years of full-time experience in fraud strategy, fraud analytics, payments risk, data science, financial crime, risk operations strategy, or a related technical risk function

- Mandatory SQL experience, with the ability to independently query data, validate hypotheses, assess rule or control performance, and support fraud strategy development.

- Exposure to data science, statistics, experimentation, model evaluation, Python/R, feature development, segmentation, or analytical methods used in fraud or risk decisioning.

- Experience working with fraud operations, risk analytics, data science, product, engineering, compliance, or payment operations stakeholders.

- Bachelor’s degree in Data Science, Analytics, Statistics, Finance, Economics, Risk Management, Criminal Justice, Computer Science, or related field, or equivalent practical experience.

KNOWLEDGE/SKILLS/ABILITIES
Required:

- Strong analytical curiosity and ability to connect fraud signals across