DS Analytics III - Fraud & Risk

Role Overview: Expedia Group is looking for a high-performing Fraud Data Scientist to join their team. As a Fraud Data Scientist, you will be responsible for leading fraud analytics projects, detecting and preventing fraud using advanced analytics and machine learning, collaborating with stakeholders, and mentoring junior team members. You will play a critical role in shaping how the company detects and responds to emerging threats in order to protect customers and maintain the integrity of the platform. Key Responsibilities: - Lead fraud analytics projects from scoping to delivery, including feature engineering, model evaluation, and business impact analysis. - Collaborate with stakeholders to refine business questions, define success metrics, and ensure alignment with fraud prevention goals. - Apply supervised and unsupervised learning to detect fraud patterns, reduce false positives, and improve customer experience. - Design and evaluate experiments (e.g., A/B tests, pre/post analysis, causal impact) to measure the effectiveness of fraud interventions. - Build and maintain modular, scalable code and contribute to shared analytics libraries and documentation. - Translate complex findings into clear, actionable insights through storytelling, dashboards, and presentations. - Mentor junior analysts and contribute to a culture of collaboration, learning, and continuous improvement. - Leverage AI tools and emerging technologies to work smarter, automate repetitive tasks, and accelerate insight generation. Qualifications Required: - Bachelor's in Math/Statistics/Computer Science or related with 4+ years of work experience in data science/analytics (preferably fraud, risk, or Trust & Safety field), or - Master's in Math/Statistics/Computer Science or related with 2+ years of work experience in data science/analytics (preferably fraud, risk, or Trust & Safety field) - Proven track record of delivering data-driven insights that led to measurable business outcomes. - Proficient in Python, SQL, and/or R for data manipulation, modeling, and visualization. - Experience working with large-scale datasets and building reproducible analytics pipelines. - Solid understanding of statistical methods (e.g., regression, ANOVA, A/B testing, causal inference). - Familiarity with machine learning techniques (e.g., classification, clustering, anomaly detection) and their application in fraud detection. - Strong data visualization skills using tools like Plotly, Seaborn, ggplot, or similar. - Understanding inclusive design principles and ability to communicate insights to both technical and non-technical audiences.,