Data Scientist

Data Scientist - Risk and Fraud AnalyticsIn this role, you will analyze large and/or complex datasets, develop predictive models, and derive actionable insights that drive key business decisions.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.·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.Required QualificationsBachelor's or master's degree in data science, Computer Science, Statistics, Mathematics, Economics or a related field.10+ years of professional experience in data scienceProficient in Python, SQL, SAS and machine learning techniquesExperience in responsible use of AI if used in solution design·Strong analytical skills and the ability to identify patterns and trends from dataExperience 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.Required/Desired SkillsSkill - Required /Desired - Amount - of ExperienceFamiliarity with graph analytics or network-based fraud detection tools. - Highly desired - 0Knowledge of regulatory frameworks and compliance issues related to fraud and financial crime. - Highly desired - 0Strong communication skills with the ability to explain technical solutions to non-technical stakeholders. - Highly desired - 0Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Mathematics, Economics or a related field. - Required - 0Professional experience in data science. - Required - 10 - YearsProficient in Python, SQL, SAS and machine learning techniques. - Required - 5 - YearsExperience working with large datasets and cloud platforms (e.g., AWS, GCP, Azure). - Required - 5 - YearsUnderstanding of supervised and unsupervised fraud detection techniques, including anomaly detection, behavioral modeling, and network analysis. - Required - 0Experience with visualization tools like Tableau and Power BI. - Required - 5 - YearsExperience in responsible use of AI if used in solution design. - Required - 5 - YearsStrong analytical skills and the ability to identify patterns and trends from data. - Required - 0