Data Scientist

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; Translate business 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

Requirements

Bachelor’s or master’s 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