Fraud Data Scientist Jobs in Rochester, NY (NOW HIRING)

To thrive as a Fraud Data Scientist, you need strong analytical skills in statistics, machine learning, and data analysis, typically backed by a degree in data science, mathematics, or a related field. Familiarity with programming languages like Python or R, experience with SQL databases, and knowledge of fraud detection tools such as SAS, Hadoop, or relevant certifications like CFE are highly valued. Excellent problem-solving ability, communication skills, and the capacity to work collaboratively with cross-functional teams are important soft skills. These abilities are crucial for identifying and mitigating fraudulent activities while ensuring clear collaboration and actionable insights in a high-stakes financial environment.

A Fraud Data Scientist analyzes transactional and behavioral data to detect, prevent, and mitigate fraudulent activities. They use machine learning models, statistical analysis, and anomaly detection techniques to identify suspicious patterns in financial, e-commerce, or other data-heavy industries. Their role involves working with large datasets, collaborating with fraud investigators, and continuously improving fraud detection systems to minimize financial losses and risks.

A Fraud Data Scientist's day often involves analyzing large datasets to detect suspicious patterns, developing and validating machine learning models to predict fraudulent activity, and collaborating with other teams such as compliance and risk management. Additionally, they may respond to real-time fraud alerts, participate in meetings to refine detection strategies, and prepare reports for stakeholders. The role combines technical analysis with ongoing learning about emerging fraud trends, making every day dynamic and intellectually challenging. Teamwork and adaptability are essential, as you'll frequently coordinate with engineers and business leaders to continually enhance fraud prevention efforts.