ChaseNet Data Scientist

About ChaseNet

ChaseNet is JPMorganChase's proprietary closed-loop payments network that connects Chase-issued cardholders directly with merchants acquired by Chase Merchant Services. By routing eligible transactions through a single Chase-owned rail rather than the traditional open-loop network, ChaseNet simplifies the economics of card acceptance, enables customized pricing constructs, and delivers richer transaction data to both merchants and the firm. ChaseNet supports some of the largest merchants in the United States and plays a central role in Chase's merchant services growth strategy.

As a ChaseNet Data Scientist, Associate in the ChaseNet Analytics team, you will work with large and complex payments datasets to generate insights that inform pricing strategy, merchant profitability, portfolio performance, and strategic decision-making.

Job Responsibilities

• Manage, transform, and analyze large payments datasets to support ChaseNet pricing, profitability, and portfolio optimization initiatives.
• Design, build, and maintain recurring and ad-hoc reporting that provides visibility into ChaseNet performance, revenue drivers, and merchant economics.
• Develop analytical frameworks and models to evaluate pricing scenarios, interchange impacts, repricing opportunities, and merchant business cases.
• Partner with product, pricing, finance, and relationship management teams to translate business questions into structured analyses and clear recommendations.
• Write efficient, well-documented SQL and Python code to extract, process, and analyze data from large-scale payments platforms.
• Support governance, quality control, and process improvement across analytics workflows, including automation and documentation.
• Communicate findings clearly to both technical and non-technical audiences through written narratives, visualizations, and presentations.
• Contribute to innovation initiatives, including the use of AI and emerging technologies to improve analytical capabilities and decision support.

Required qualifications, capabilities, and skills:

• Undergraduate degree with 3+ years of experience
• Understanding of SQL-based coding and Python.
• Consulting orientation with the ability to influence both internal and external business partners.
• Understanding of the payments value chain, including merchants, brands, acquirers, networks, and issuers.
• Proven experience working in large teams in an advanced analytical environment.
• Proven experience with emerging technologies related to insights and the ability to do rapid prototyping (in Tableau).
• Ability to synthesize data sets into streamlined, clear, and actionable messages.
• Experience creating business initiatives and communicating ideas with a digitally focused and data-driven mindset.
• Proven ability to develop relationships with a wide array of partners, executives, and customers.

Preferred qualifications, capabilities, and skills:

• AI/ML and automation skills, AI/ML solutions creation, and software engineering skills preferred.
• Experience in the payments industry and/or technology-enabled industries (preferred but not required).
• Working knowledge of the credit card value chain, including the roles of issuers, acquirers, networks, processors, and merchants.
• Familiarity with network fees and interchange economics, including how interchange categories, assessments, and network-level fees are structured.
• Familiarity with processing fees and merchant pricing constructs such as interchange-plus, blended pricing, and tiered pricing.

Responsibilities

Manage, transform, and analyze large payments datasets to support ChaseNet pricing, profitability, and portfolio optimization initiatives.

Design, build, and maintain recurring and ad-hoc reporting that provides visibility into ChaseNet performance, revenue drivers, and merchant economics.

Develop analytical frameworks and models to evaluate pricing scenarios, interchange impacts, repricing opportunities, and merchant business cases.

Partner with product, pricing, finance, and relationship management teams to translate business questions into structured analyses and clear recommendations.

Write efficient, well-documented SQL and Python code to extract, process, and analyze data from large-scale payments platforms.

Support governance, quality control, and process improvement across analytics workflows, including automation and documentation.

Communicate findings clearly to both technical and non-technical audiences through written narratives, visualizations, and presentations.

Contribute to innovation initiatives, including the use of AI and emerging technologies to improve analytical capabilities and decision support.

Qualifications

Experience Requirements

Undergraduate degree required; MBA preferred.

Understanding of SQL-based coding and Python.

Consulting orientation with the ability to influence both internal and external business partners.

3+ years of experience in the payments industry and/or technology-enabled industries (preferred but not required).

Understanding of the payments value chain, including merchants, brands, acquirers, networks, and issuers (preferred but not required).

Proven experience working in large teams in an advanced analytical environment.

Proven experience with emerging technologies related to insights and the ability to do rapid prototyping (in Tableau).

Ability to synthesize data sets into streamlined, clear, and actionable messages.

Experience creating business initiatives and communicating ideas with a digitally focused and data-driven mindset.

Proven ability to develop relationships with a wide array of partners, executives, and customers.

Preferred Knowledge

AI/ML and automation skills, AI/ML solutions creation, and software engineering skills preferred.

Working knowledge of the credit card value chain, including the roles of issuers, acquirers, networks, processors, and merchants.

Familiarity with network fees and interchange economics, including how interchange categories, assessments, and network-level fees are structured.

Familiarity with processing fees and merchant pricing constructs such as interchange-plus, blended pricing, and tiered pricing.

Exposure to ChaseNet or other closed-loop or on-us payment constructs.