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About This Role
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.
Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Worldpay, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $192,890 based on 789 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Worldpay AI Hiring
Worldpay has 3 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span Cincinnati, OH, US, Atlanta, GA, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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