What are the responsibilities and job description for the Data Scientist, Intern position at Netspend?
About The Company
Netspend Corporation is a global, vertically-integrated financial services and technology company dedicated to the delivery of innovative financial empowerment solutions to consumers worldwide. Netspend's financial products and services span prepaid, debit, cross-border payments, and loyalty solutions for consumers and enterprise partners.
Netspend provides prepaid and debit account solutions that connect customers with secure, convenient access to global payment networks so they can manage their money and make everyday purchases. With a nationwide U.S. retail network, customers can purchase and reload Netspend products at 130,000 reload points and over 100,000 distributing locations.
Since our founding in 1999 by industry pioneers, Netspend products have processed billions of dollars in transaction volume and served millions of customers worldwide. The company is headquartered in Austin, Texas with employees worldwide.
The Data Scientist, Intern is a junior professional responsible for applying data science methodologies to extract insights, build predictive models, and support data-driven decision-making across the organization. At this level, they will work on developing professional expertise by applying company policies and statistical concepts to resolve a variety of data challenges. They will exercise judgment within defined procedures to determine the appropriate analytical actions, working under general instruction to build standard models and uncover trends that support broader strategic initiatives.
Essential Functions & Responsibilities
Education: Progressing towards a degree in Data Science, Computer Science, Statistics, Mathematics, or a highly quantitative field. Experience: None Technical Skills: Proficiency in programming languages for data science (Python or R).
Netspend Corporation is a global, vertically-integrated financial services and technology company dedicated to the delivery of innovative financial empowerment solutions to consumers worldwide. Netspend's financial products and services span prepaid, debit, cross-border payments, and loyalty solutions for consumers and enterprise partners.
Netspend provides prepaid and debit account solutions that connect customers with secure, convenient access to global payment networks so they can manage their money and make everyday purchases. With a nationwide U.S. retail network, customers can purchase and reload Netspend products at 130,000 reload points and over 100,000 distributing locations.
Since our founding in 1999 by industry pioneers, Netspend products have processed billions of dollars in transaction volume and served millions of customers worldwide. The company is headquartered in Austin, Texas with employees worldwide.
The Data Scientist, Intern is a junior professional responsible for applying data science methodologies to extract insights, build predictive models, and support data-driven decision-making across the organization. At this level, they will work on developing professional expertise by applying company policies and statistical concepts to resolve a variety of data challenges. They will exercise judgment within defined procedures to determine the appropriate analytical actions, working under general instruction to build standard models and uncover trends that support broader strategic initiatives.
Essential Functions & Responsibilities
- Predictive Modeling & Analysis: Under guidance, build, test, and deploy standard machine learning models (e.g., regression, classification, clustering) to address specific business questions under the guidance of senior team members
- Data Preparation & Engineering: Extract, clean, and transform structured and unstructured data from various sources (using SQL and Python/R) to prepare it for exploratory data analysis (EDA) and model training
- Model Evaluation: Conduct routine testing and validation of existing data models. Apply professional judgment to identify performance anomalies and recommend tuning or remediation steps
- Data Visualization & Reporting: Translate complex analytical findings into clear, actionable insights. Develop dashboards and reports using visualization tools (e.g., Tableau, PowerBI, matplotlib) to present data clearly to business stakeholders.
- Process Improvement: Develop professional expertise by identifying opportunities to modify, adapt, or automate data processing and modeling workflows to improve efficiency
- Cross-Functional Collaboration: Partner with data engineers, product managers, and business units to understand their objectives and translate them into technical data science requirements
- Relationship Management: Develop and maintain stable working relationships with immediate colleagues, supervisors, and cross-functional teams to foster a cohesive, data-literate culture
- Project Support: Assist senior data scientists (P3/P4) on complex, large-scale predictive modeling projects and participate in broader data governance and analytics workstreams as assigned
- All other duties assigned
- Strong ability to write SQL queries for data extraction and manipulation
- Hands-on experience with core machine learning libraries (e.g., scikit-learn, pandas, NumPy)
- Experience with data visualization software (e.g., Tableau, Power BI,Qlik) and version control (e.g., Git)
- Knowledge & Application: Develops professional expertise and applies company policies to resolve a variety of data science and modeling issues. Modifies analytical processes as required, under guidance. Requires minimal instruction on routine work and general instructions on new assignments
- Complexity & Problem Solving: Works on data problems of moderate scope, using defined procedures and judgment to determine the appropriate action. Requires analysis of datasets and situations by reviewing a variety of factors
- Collaboration & Interaction: Focuses on developing and maintaining stable working relationships with immediate colleagues, supervisors, and internal business partners. Adapts style to communicate standard technical advice and analytical results clearly