Job Description

Join a Payments Data and Analytics team that builds data products used to improve how clients run and grow their businesses. You will work on high-impact analytics and generative AI solutions across large, complex payment data sets. You will collaborate with product, engineering, and business partners to deliver insights and decision tools. This role offers opportunities to use modern data platforms and develop client-facing solutions at scale.

As a Data Scientist-Senior Associate in Payments Data and Analytics, you execute analytics initiatives that improve payment performance and enable product innovation. You translate business goals into data requirements, models, and measurable deliverables. You develop analyses, data products, and generative AI solutions that produce actionable insights for stakeholders and clients. You partner with cross-functional teams to productionize resilient analytics and AI applications. You contribute to continuous improvement through strong documentation, testing, and change-control practices.

You will work with large-scale structured data and build reusable datasets, queries, and visualizations that support decision-making. The role requires strong analytics fundamentals, practical software engineering habits, and comfort working end-to-end from problem definition through delivery and support. Success is measured by clear business outcomes, reliable production solutions, and strong stakeholder partnership.

Job responsibilities

• Define analytic projects by translating business objectives into clear problem statements, success metrics, and delivery plans.
• Develop analyses and deliverables that align to agreed requirements, timelines, and stakeholder expectations.
• Build datasets, queries, and reusable tables to enable scalable analytics and reporting.
• Create visualizations and decision-support tools that communicate insights to technical and non-technical audiences.
• Recommend actionable performance improvements to clients based on payment and transaction behavior patterns.
• Productionize interactive analytics and AI applications with a focus on resiliency, usability, and supportability.
• Identify opportunities to enhance existing analytics products and propose improvements grounded in data.
• Partner with data owners and platform teams to evolve data models for future analytics and product capabilities.
• Collaborate with unit leaders, end users, and development teams to confirm business value and prioritize work.
• Apply quality assurance, change-control, and testing practices for updates to code, models, and data products.
• Support users by addressing questions, incorporating feedback, and improving documentation and training materials.

Required qualifications, capabilities, and skills

• Bachelor’s or master’s degree in engineering, computer science, statistics, mathematics, or a related quantitative field.
• Four or more years of experience delivering analytics in a big data environment in a self-directed role.
• Experience using SQL (Snowflake) and Python to build analyses and data products.
• Experience applying generative AI tools or techniques to analytics, automation, or decision-support use cases.
• Understanding of algorithms, data structures, and software engineering fundamentals.
• Demonstrated ability to define key performance indicators (KPIs) and performance metrics from data.
• Experience sourcing, assessing, and shaping data for analysis, including improving data structures and query performance.
• Ability to document analytics requirements, assumptions, and deliverables in partnership with stakeholders.
• Strong communication skills with the ability to present insights clearly to varied audiences.
• Strong problem-solving skills with the ability to translate strategy into measurable deliverables.

Preferred qualifications, capabilities, and skills

• Experience in payments, merchant services, or adjacent transaction-driven industries.
• Experience building and evaluating generative AI solutions (for example, prompt design, retrieval-augmented generation patterns, or model evaluation).
• Experience deploying analytics or AI applications into production with monitoring and support practices.
• Experience building data visualizations and dashboards for business users.
• Experience partnering with product and engineering teams to deliver analytics capabilities on shared platforms.