Job Description Engagement Type Contract 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 • 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. Required/Desired Skills SkillRequired/DesiredAmountof Experience Familiarity with graph analytics or network-based fraud detection tools. Highly desired 0 Knowledge of regulatory frameworks and compliance issues related to fraud and financial crime. Highly desired 0 Strong communication skills with the ability to explain technical solutions to non-technical stakeholders. Highly desired 0 Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, Economics or a related field. Required 0 Professional experience in data science. Required 10 Years Proficient in Python, SQL, SAS and machine learning techniques. Required 5 Years Experience working with large datasets and cloud platforms (e.g., AWS, GCP, Azure). Required 5 Years Understanding of supervised and unsupervised fraud detection techniques, including anomaly detection, behavioral modeling, and network analysis. Required 0 Experience with visualization tools like Tableau and Power BI. Required 5 Years Experience in responsible use of AI if used in solution design. Required 5 Years Strong analytical skills and the ability to identify patterns and trends from data. Required 0 Recruiter Instructions Recruiter Instructions Questions Description Question 1 Absences greater than two weeks MUST be approved by CAI management in advance, and contact information must be provided to CAI so that the resource can be reached during his or her absence. The Client has the right to dismiss the resource if he or she does not return to work by the agreed upon date. Do you accept this requirement? Question 2 Please list candidate's email address that will be used when submitting E-RTR. Question 3 The maximum mark-up for this engagement’s SRP rate is 35%. To be competitive on pricing, a mark-up below the 35% threshold is suggested. Do you agree to propose a mark-up at or below 35%? Question 4 This assignment is contingent upon customer renewal and availability of adequate funding. Do you accept this requirement? Question 5 The selected candidate will be expected to start their engagement no later than 2 weeks (10 business days) from the client’s selection date. Do you accept this requirement? Question 6 HYBRID work option: However, the selected candidate must be available to report onsite as directed by the client. Do you accept this requirement?