Data Scientist

Job ID: GA-767893

Hybrid/Local Data Scientist (12+)with graph analytics, financial fraud detection, anomaly detection, behavioral modeling, Python, SQL, SAS, AI/ML, AWS/Azure/Google Cloud Platform, Tableau Power BI experience

Location: Atlanta, GA (DHS)
Duration: 11 Months
Position: 1(2)

Skills:
Familiarity with graph analytics or network-based fraud detection tools. Highly desired
Knowledge of regulatory frameworks and compliance issues related to fraud and financial crime. Highly desired
Strong communication skills with the ability to explain technical solutions to non-technical stakeholders. Highly desired
Bachelor s or Master s degree in Data Science, Computer Science, Statistics, Mathematics, Economics or a related field. Required
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, Google Cloud Platform, Azure). Required 5 Years
Understanding of supervised and unsupervised fraud detection techniques, including anomaly detection, behavioral modeling, and network analysis. Required
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

Job 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, 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.