GA DHS - Data Scientist

Short Description In this role, you will analyze large and/or complex datasets, develop predictive models, and derive actionable insights that drive key business decisions. Complete Description We are seeking a highly analytical and detail-oriented Data Scientist with experience in Risk and Fraud analytics to join our growing team. This role will focus on developing and deploying machine learning models, statistical methods, and data-driven strategies to detect risky behaviors and prevent fraudulent activities across our products and services. Key Responsibilities • Collect, clean, and analyze large, complex datasets from multiple sources. • Develop predictive models and machine learning algorithms to support decision-making and improve business performance. • Translatebusiness problems into data-driven solutions with measurable impact. • Develop and deploy machine learning models to detect, predict, and prevent fraudulent transactions and behavior patterns. • Analyze large volumes of structured and unstructured data from multiple sources to identify fraud trends and root causes. • Collaborate with fraud operations, engineering, and compliance teams to implement real-time fraud detection solutions. • Design and monitor KPIs to evaluate model performance and improve fraud detection systems over time. • Conduct deep-dive investigations into fraud cases, creating detailed reports and actionable insights. • Stay current with emerging fraud techniques, industry best practices, and data science tools. Required Qualifications • Bachelors or masters degree in data science, Computer Science, Statistics, Mathematics, Economics or a related field. • 10+ years of professional experience in data science • Proficient in Python, SQL, SAS and machine learning techniques • Experience in responsible use of AI if used in solution design • Strong analytical skills and the ability to identify patterns and trends from data • Experience working with large datasets and cloud platforms (e.g., AWS, GCP, Azure). • Strong understanding of supervised and unsupervised fraud detection techniques, including anomaly detection, behavioral modeling, and network analysis. • Experience with visualization tools like Tableau and Power BI.