Payments Data Scientist

Global Payments Inc.•Cincinnati, OH

1d•$87,500 - $139,250

About The Position

Applicants must be authorized to work in the United States on a permanent basis; visa sponsorship, including future sponsorship for OPT holders, is not available for this position. Ready to take your career global? Turn complex data into insights and intelligent products that make everyday commerce better. As a Data Scientist at Global Payments, you’ll apply machine learning, statistical modeling and strong engineering skills to solve meaningful business and client challenges. You’ll join a connected team where you can learn from experienced data scientists, contribute your ideas and build your expertise. You’ll have the opportunity to work with large, complex datasets while shaping practical solutions that balance analytical accuracy, performance and business value. About the Team The Payment Strategic Routing team uses data, analytics, and technology to optimize payment performance and drive better outcomes for clients. We partner across business and technology teams to develop innovative solutions that improve debit routing efficiency, reduce costs and maximize success rates. We are seeking a Data Scientist I to apply analytics and AI/ML techniques to solve business problems and support data-driven decision making. This role involves collaborating with cross-functional teams to develop insights, build predictive models, and deliver solutions that improve operational and business performance. The ideal candidate combines strong analytical skills, technical expertise, and a curiosity for solving complex problems.

Requirements

A bachelor's or master’s degree in Data Science, Statistics, Mathematics, Applied Mathematics, Computer Science, Engineering, Economics, or a related quantitative field.
Experience with data analysis, statistical methods, machine learning, and predictive modeling.
Proficiency in data science tools and programming languages such as Python, R, and SQL.
Experience working with large datasets and modern analytics tools; experience with cloud-based data platforms is a plus.
Knowledge of machine learning, artificial intelligence, statistical modeling, data visualization, and advanced analytics techniques.
Strong analytical, decision-making, problem-solving, and critical-thinking skills with the ability to translate complex challenges into practical solutions.
Experience collaborating with cross-functional teams to deliver data-driven solutions.
Excellent written and verbal communication skills, with the ability to communicate technical concepts and insights to both technical and nontechnical audiences.
Curiosity, eagerness to learn, and a passion for leveraging data and analytics to solve business problems and create measurable value.

Nice To Haves

Experience with cloud-based data platforms is a plus.
Knowledge of financial services, payments, or related industries is a plus.

Responsibilities

Transform complex data into meaningful insights that guide decision-making, uncover opportunities, and create value for internal teams, partners, and clients.
Analyze and interpret data to support business decisions and develop actionable recommendations.
Build, validate, and enhance data-driven products using machine learning, artificial intelligence, and statistical modeling to improve business and client outcomes.
Develop data visualizations, dashboards, and reports that clearly communicate findings to technical and nontechnical audiences.
Partner with business and technology teams to understand requirements, define use cases, and translate business needs into analytical solutions.
Shape large and complex datasets into reliable, usable formats while ensuring data quality and integrity.
Apply the right technical approach using tools and programming languages such as Python, R, and SQL.
Support the deployment, monitoring, and continuous improvement of analytical models and data products.
Influence analytical solution design by contributing recommendations and collaborating with experienced data scientists and cross-functional teams.
Protect model quality and performance through documentation, maintainable code, and ongoing monitoring.
Make pragmatic, business-aware decisions by balancing sophistication and accuracy with implementation effort, performance, and cost.