Data Scientist

Overview

On Site
Hybrid
Depends on Experience
Accepts corp to corp applications
Contract - Independent
Contract - W2
Contract - 11 Month(s)
No Travel Required

Skills

Data Scientist
graph analytics
financial fraud detection
anomaly detection
behavioral modeling
Python
SQL
SAS
AI
ML
AWS
Azure
GCP
Tableau Power BI
Software Design
Tableau
Unstructured Data
Visualization
Network Analysis
Management
Mathematics
Microsoft Azure
Microsoft Power BI
Good Clinical Practice
Data Science
Decision-making
Economics
Network
ROOT
RTR
FOCUS
Finance
Fraud
Google Cloud Platform
KPI
Machine Learning (ML)
Attention To Detail
Cloud Computing
Collaboration
Communication
Algorithms
Amazon Web Services
Analytical Skill
Analytics
Artificial Intelligence
Computer Science
Real-time
Regulatory Compliance
Statistics

Job Details

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.

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