Data Scientist (767893)

Role:Data Scientist (767893)
Client: GA DHS

Location: 47 Trinity Ave SW, Atlanta, GA 30334 Hybrid

Max pay Rate: $75/hr on W2 - Per Hour

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, Google Cloud Platform, 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.

Regards,