Data Scientist Visa Sponsorship Jobs in Georgia
Data scientist visa sponsorship jobs in Georgia are concentrated in Atlanta, where employers like NCR Voyix, Cox Enterprises, and Delta Air Lines hire for analytics and machine learning roles. The state's growing fintech sector, Emory University research ties, and Centers for Disease Control proximity create steady demand across healthcare, finance, and logistics.
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Ready to take your career global?
Make your mark at one of the biggest names in payments. We are seeking a hands-on Lead Risk Data Scientist & ML Engineer to own the full lifecycle of fraud detection and risk models. This role combines deep data science expertise with practical AI/agentic workflow experience and the infrastructure knowledge needed to ship at scale.
What You’ll Own
In this role, you'll own the end-to-end delivery of detection models and AI-assisted workflows that power fraud, credit, and AML risk operations. You'll drive model performance through the full lifecycle, from design and validation through production deployment and continuous optimization. You'll translate regulatory requirements and operational needs into detection strategies and technical execution plans, partner across Risk, Compliance, and Technology to ensure alignment, and lead project teams through complex, ambiguous detection challenges. This is a hands-on role that combines deep technical leadership with pragmatic problem-solving in a small, high-impact team.
Model Development & Deployment (End-to-End)
- Own the full lifecycle of ML models: design, development, validation, deployment, and serving in production
- Lead model performance monitoring and continuous refinement using production data and investigation outcomes
- Ensure models are explainable, auditable, and aligned with regulatory expectations
- Design and oversee scalable batch and real-time data pipelines supporting model development and serving
AI & Agentic Workflows
- Design and deploy AI-assisted analyst workflows using LLMs and agentic frameworks
- Guide the development of agent-based systems that augment human decision-making in risk operations
- Work at the pilot/proof-of-concept stage, establishing best practices for scale
Detection Strategy & Performance
- Define and refine detection strategies based on emerging fraud patterns and regulatory requirements
- Maintain and monitor key performance metrics (precision, recall, false positives, alert quality)
- Influence tradeoff decisions between detection coverage, operational cost, and false positive rates
Governance & Regulatory Alignment
- Define governance standards for model development, validation, documentation, and change management
- Ensure compliance with regulatory expectations (BSA/AML, OFAC, FinCEN, SR 11-7)
- Partner with Model Risk Management and Compliance to support validation and regulatory reviews
Cross-Functional Partnership
- Serve as the primary technical partner to Fraud Operations, Compliance, and Technology teams
- Translate regulatory and operational requirements into technical execution plans
- Drive alignment across teams to enable effective detection capability implementation
Team Leadership & Project Ownership
- Lead cross-functional project teams through ML model and AI workflow development, from conception to deployment
- Establish clear priorities, performance expectations, and delivery accountability for project work
- Provide technical guidance and mentorship to data scientists and engineers executing on risk initiatives
- Build and strengthen team capabilities across detection modeling, data engineering, and AI/agentic systems
What you’ll bring:
Experience
- 7+ years in data science, machine learning or MLOps
- Proven experience developing, deploying, and maintaining detection models (fraud, AML, or credit risk) in production environments
- Hands-on experience with AI-assisted workflows, LLMs, and agentic frameworks (including pilot-stage deployments)
- Experience in regulated financial services or fintech environments preferred
- Exposure to model risk management frameworks (SR 11-7) and regulatory interactions
Technical & Domain Expertise
- Strong proficiency in Python and SQL
- MLOps experience: Git, GitHub Actions, CI/CD practices, model monitoring, retraining pipelines, infrastructure automation
- Hands-on experience with data science platforms (Databricks, Snowflake, AWS SageMaker)
- AWS ecosystem expertise: SageMaker, Glue, Lambda, EventBridge, and related services
- Familiarity with LLM and agentic frameworks: foundational models (Claude, GPT, etc.), agent orchestration tools (AWS AgentCore, LangChain, etc.)
- Understanding of fraud typologies, AML transaction monitoring methodologies, and detection system design
Leadership Profile
- Resourceful and versatile: thrives in a small, fast-moving team; comfortable wearing multiple hats and delivering with constrained resources
- Startup mentality: pragmatic problem-solver who ships solutions; bias toward execution and measurable outcomes
- Combines technical depth with collaborative leadership. Guides project teams through ambiguous problems and drives clarity, structure, and delivery
- Collaborates effectively across Risk, Compliance, and Technology functions; comfortable operating in ambiguity and translating strategy into action
About the team
Our inclusive and global teams win together every day. We’re proud to have the best minds in the industry, who you can learn from as you grow your career. The people, the energy, the connections – it’s unmatched. Come and be part of an ever-evolving company and get dynamic opportunities that go beyond borders.
What makes a Globalpayer?
Globalpayers think like a client, act like an owner and win as one team. We’re curious and innovative – always finding better ways to deliver impact. We empower each other to make decisions, and it’s our passion that drives excellence in everything we set out to do.
LI-BJ1
EEOC Statement
Worldpay is an equal opportunity employer. We evaluate qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, marital status, genetic information, national origin, disability, veteran status, and other protected characteristics. The EEO is the Law poster is available here.
If you are made a conditional offer of employment and will be working in the United States, you will be required to undergo a drug test. In developing this job description care was taken to include all competencies and requirements needed to successfully perform the position. Reasonable accommodations will be provided for individuals with qualified disabilities both during the hiring process, as well as to allow the individual to perform the essential functions of the job, if hired.
Job Description
Ready to take your career global?
Make your mark at one of the biggest names in payments. We are seeking a hands-on Lead Risk Data Scientist & ML Engineer to own the full lifecycle of fraud detection and risk models. This role combines deep data science expertise with practical AI/agentic workflow experience and the infrastructure knowledge needed to ship at scale.
What You’ll Own
In this role, you'll own the end-to-end delivery of detection models and AI-assisted workflows that power fraud, credit, and AML risk operations. You'll drive model performance through the full lifecycle, from design and validation through production deployment and continuous optimization. You'll translate regulatory requirements and operational needs into detection strategies and technical execution plans, partner across Risk, Compliance, and Technology to ensure alignment, and lead project teams through complex, ambiguous detection challenges. This is a hands-on role that combines deep technical leadership with pragmatic problem-solving in a small, high-impact team.
Model Development & Deployment (End-to-End)
- Own the full lifecycle of ML models: design, development, validation, deployment, and serving in production
- Lead model performance monitoring and continuous refinement using production data and investigation outcomes
- Ensure models are explainable, auditable, and aligned with regulatory expectations
- Design and oversee scalable batch and real-time data pipelines supporting model development and serving
AI & Agentic Workflows
- Design and deploy AI-assisted analyst workflows using LLMs and agentic frameworks
- Guide the development of agent-based systems that augment human decision-making in risk operations
- Work at the pilot/proof-of-concept stage, establishing best practices for scale
Detection Strategy & Performance
- Define and refine detection strategies based on emerging fraud patterns and regulatory requirements
- Maintain and monitor key performance metrics (precision, recall, false positives, alert quality)
- Influence tradeoff decisions between detection coverage, operational cost, and false positive rates
Governance & Regulatory Alignment
- Define governance standards for model development, validation, documentation, and change management
- Ensure compliance with regulatory expectations (BSA/AML, OFAC, FinCEN, SR 11-7)
- Partner with Model Risk Management and Compliance to support validation and regulatory reviews
Cross-Functional Partnership
- Serve as the primary technical partner to Fraud Operations, Compliance, and Technology teams
- Translate regulatory and operational requirements into technical execution plans
- Drive alignment across teams to enable effective detection capability implementation
Team Leadership & Project Ownership
- Lead cross-functional project teams through ML model and AI workflow development, from conception to deployment
- Establish clear priorities, performance expectations, and delivery accountability for project work
- Provide technical guidance and mentorship to data scientists and engineers executing on risk initiatives
- Build and strengthen team capabilities across detection modeling, data engineering, and AI/agentic systems
What you’ll bring:
Experience
- 7+ years in data science, machine learning or MLOps
- Proven experience developing, deploying, and maintaining detection models (fraud, AML, or credit risk) in production environments
- Hands-on experience with AI-assisted workflows, LLMs, and agentic frameworks (including pilot-stage deployments)
- Experience in regulated financial services or fintech environments preferred
- Exposure to model risk management frameworks (SR 11-7) and regulatory interactions
Technical & Domain Expertise
- Strong proficiency in Python and SQL
- MLOps experience: Git, GitHub Actions, CI/CD practices, model monitoring, retraining pipelines, infrastructure automation
- Hands-on experience with data science platforms (Databricks, Snowflake, AWS SageMaker)
- AWS ecosystem expertise: SageMaker, Glue, Lambda, EventBridge, and related services
- Familiarity with LLM and agentic frameworks: foundational models (Claude, GPT, etc.), agent orchestration tools (AWS AgentCore, LangChain, etc.)
- Understanding of fraud typologies, AML transaction monitoring methodologies, and detection system design
Leadership Profile
- Resourceful and versatile: thrives in a small, fast-moving team; comfortable wearing multiple hats and delivering with constrained resources
- Startup mentality: pragmatic problem-solver who ships solutions; bias toward execution and measurable outcomes
- Combines technical depth with collaborative leadership. Guides project teams through ambiguous problems and drives clarity, structure, and delivery
- Collaborates effectively across Risk, Compliance, and Technology functions; comfortable operating in ambiguity and translating strategy into action
About the team
Our inclusive and global teams win together every day. We’re proud to have the best minds in the industry, who you can learn from as you grow your career. The people, the energy, the connections – it’s unmatched. Come and be part of an ever-evolving company and get dynamic opportunities that go beyond borders.
What makes a Globalpayer?
Globalpayers think like a client, act like an owner and win as one team. We’re curious and innovative – always finding better ways to deliver impact. We empower each other to make decisions, and it’s our passion that drives excellence in everything we set out to do.
LI-BJ1
EEOC Statement
Worldpay is an equal opportunity employer. We evaluate qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, marital status, genetic information, national origin, disability, veteran status, and other protected characteristics. The EEO is the Law poster is available here.
If you are made a conditional offer of employment and will be working in the United States, you will be required to undergo a drug test. In developing this job description care was taken to include all competencies and requirements needed to successfully perform the position. Reasonable accommodations will be provided for individuals with qualified disabilities both during the hiring process, as well as to allow the individual to perform the essential functions of the job, if hired.
Data Scientist Job Roles in Georgia
See all 173+ Data Scientist Jobs in Georgia
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Search Data Scientist Jobs in GeorgiaData Scientist Jobs in Georgia: Frequently Asked Questions
Which companies in Georgia sponsor visas for data scientists?
Atlanta-based employers are the primary sponsors for data scientist roles in Georgia. Companies like Delta Air Lines, Cox Enterprises, NCR Voyix, and Home Depot regularly file H-1B visa petitions for data science positions. Healthcare organizations tied to the Emory and Georgia Tech ecosystems, along with fintech firms concentrated in the Buckhead and Midtown corridors, also sponsor data scientists with meaningful frequency.
Which visa types are most common for data scientist roles in Georgia?
The H-1B is the most common visa for data scientists in Georgia, given that the role typically qualifies as a specialty occupation requiring at least a bachelor's degree in computer science, statistics, or a related field. Candidates already holding OPT or STEM OPT can work for a sponsoring employer before the H-1B petition is filed. Some senior research-track roles may also see O-1 visa petitions for candidates with exceptional publication or award records.
Which cities in Georgia have the most data scientist visa sponsorship jobs?
Atlanta accounts for the overwhelming majority of data scientist sponsorship jobs in Georgia. Midtown and Buckhead host technology and financial services employers, while the Emory University and Georgia Tech corridors in the northeast part of the city generate demand for research-oriented data roles. Smaller concentrations exist in Athens and Savannah, but candidates focused on sponsorship will find the broadest opportunity within the Atlanta metro.
How to find data scientist visa sponsorship jobs in Georgia?
Migrate Mate is built specifically for international candidates and filters data scientist roles in Georgia by visa sponsorship history, so you are not sorting through listings from employers who do not sponsor. Because sponsorship intent is not always stated in job postings, using a platform that surfaces employers with a documented H-1B filing track record in Georgia saves significant time and helps you focus your applications where sponsorship is genuinely on the table.
Are there state-specific factors that affect data scientist sponsorship in Georgia?
Georgia's concentration of Fortune 500 headquarters in Atlanta means many sponsoring employers are large enough to absorb the cost and process overhead of H-1B petitions. The Georgia Tech pipeline produces a consistent pool of international graduates in data science and machine learning, which normalizes sponsorship among tech and analytics employers in the region. The presence of the CDC and Emory also creates a distinct cluster of public health and epidemiology data roles that can support sponsorship for candidates with relevant research backgrounds.
What is the prevailing wage for sponsored data scientist jobs in Georgia?
U.S. employers sponsoring a visa must pay at least the prevailing wage, which is what workers in the same role, area, and experience level typically earn. The Department of Labor sets this rate to make sure companies aren't hiring foreign workers simply because they'd accept lower pay than a U.S. worker. It varies by job title, location, and experience. You can look up current prevailing wage rates for any occupation and location using the OFLC Wage Search page.