Payments Data Scientist — ML for smarter routing & insights

Global Payments Inc.; Atlanta (GA); On-site; USD 92,000 - 139,000; Full time

Job summary

Global Payments Inc. is hiring a Data Scientist I to apply analytics and ML techniques to solve business problems and support data-driven decision making. You will collaborate with cross-functional teams to develop insights and deliver predictive models that improve operational performance.

This role covers data transformation, modeling, visualization, and deployment support, using Python, R, and SQL, with an emphasis on data quality and business value.

Qualifications

• Bachelor's or master's degree in data science, statistics, mathematics, or a related field.
• Experience with data analysis, statistics, machine learning, and predictive modeling.
• Proficiency in Python, R, and SQL.
• Experience with large datasets and cloud-based analytics is a plus.

Responsibilities

• Transform data into meaningful insights guiding decisions.
• Analyze data to support business decisions and provide actionable recommendations.
• Build and improve data-driven products using ML and statistical modeling.
• Develop dashboards and reports communicating findings to technical and non-technical audiences.
• Collaborate with cross-functional teams to translate business needs into analytical solutions.
• Ensure data quality and prepare data for modeling and deployment.

Skills

Python

R

SQL

Machine learning

Statistical modeling

Data visualization

Data analysis

Cross-functional teamwork

Education

B.S./M.S. in quantitative field

Tools

Jupyter

Cloud platforms

SQL engines

Spark

Job description

Global Payments Inc. is hiring a Data Scientist I to apply analytics and ML techniques to solve business problems and support data-driven decision making. You will collaborate with cross-functional teams to develop insights and deliver predictive models that improve operational performance.

This role covers data transformation, modeling, visualization, and deployment support, using Python, R, and SQL, with an emphasis on data quality and business value.