Unreal Gigs is hiring: Fraud Data Analyst (The Fraud Detector) in Bee Cave

Are you passionate about identifying fraudulent activity and protecting businesses from financial losses? Do you excel at analyzing patterns, developing insights, and creating proactive solutions to prevent fraud? If you're ready to leverage your analytical skills to safeguard our systems, our client has the ideal role for you. We're looking for a Fraud Data Analyst (aka The Fraud Detector) to analyze transaction data, detect potential fraud, and enhance risk mitigation strategies across the organization. As a Fraud Data Analyst at our client , you'll work with fraud prevention teams, data scientists, and risk management to develop models, identify patterns, and implement fraud detection strategies. Your role will be critical in ensuring that potential fraud is detected early and that our client's financial assets and reputation remain protected. Key Responsibilities: Analyze Transaction Data for Fraud Detection: Perform in-depth analyses of transaction data to identify patterns indicative of fraud. You'll use statistical methods to spot anomalies and flag suspicious activities for further investigation. Develop and Maintain Fraud Detection Models: Build predictive models to detect fraudulent behavior in real-time using machine learning and data analytics tools. You'll implement algorithms that help automate fraud detection. Collaborate on Risk Assessment and Prevention Strategies: Work closely with risk management teams to design fraud prevention strategies. You'll provide insights on vulnerabilities and recommend solutions to strengthen fraud defenses. Conduct Root Cause Analysis on Fraud Incidents: Investigate fraudulent activities to understand their origins and impacts. You'll perform root cause analysis to identify gaps in current controls and suggest improvements. Create and Maintain Fraud Detection Dashboards: Design and maintain dashboards using tools like Tableau or Power BI to provide real-time monitoring of fraud metrics. You'll ensure that stakeholders have access to actionable insights. Implement Data-Driven Rules and Alerts: Establish data-driven rules and thresholds for flagging suspicious activities. You'll create automated alerts that enable faster responses to potential fraud cases. Stay Updated on Fraud Trends and Techniques: Keep abreast of the latest trends in fraud schemes, methodologies, and detection technologies. You'll bring innovative strategies to improve fraud prevention and detection. Requirements Required Skills: Fraud Analysis and Pattern Recognition: Strong ability to detect patterns, anomalies, and indicators of fraud in data. Familiarity with transaction data analysis and fraud detection techniques. Predictive Modeling and Machine Learning: Experience building models for fraud detection using supervised and unsupervised learning techniques. Familiarity with tools like Python, R, or SQL for model development. Risk Assessment and Prevention: Understanding of risk management principles and strategies for fraud prevention. Ability to recommend preventive actions based on data insights. Data Visualization and Reporting: Proficiency in data visualization tools like Tableau, Power BI, or Looker for creating dashboards and presenting fraud data to stakeholders. Collaboration and Communication: Excellent communication skills for working with cross-functional teams and explaining complex fraud patterns to non-technical audiences. Educational Requirements: Bachelor's or Master's degree in Data Science, Statistics, Business Analytics, Finance, or a related field. Equivalent experience in fraud analytics or risk management may be considered. Certifications in fraud examination or analytics (e.g., Certified Fraud Examiner (CFE), Certified Analytics Professional) are advantageous. Experience Requirements: 3 years of experience in fraud analysis, risk management, or data analytics, with a proven track record of detecting and preventing fraudulent activities. Familiarity with financial services, e-commerce, or insurance fraud detection is highly beneficial. Experience with real-time data processing and anomaly detection is a plus. Benefits Health and Wellness: Comprehensive medical, dental, and vision insurance plans with low co-pays and premiums. Paid Time Off: Competitive vacation, sick leave, and 20 paid holidays per year. Work-Life Balance: Flexible work schedules and telecommuting options. Professional Development: Opportunities for training, certification reimbursement, and career advancement programs. Wellness Programs: Access to wellness programs, including gym memberships, health screenings, and mental health resources. Life and Disability Insurance: Life insurance and short-term/long-term disability coverage. Employee Assistance Program (EAP): Confidential counseling and support services for personal and professional challenges. Tuition Reimbursement: Financial assistance for continuing education and professional development. Community Engagement: Opportunities to participate in community service and volunteer activities. Recognition Programs: Employee recognition programs to celebrate achievements and milestones.