Principal Data Scientist, Payments Intelligence at WEX

Job description

About the Team/Role We are looking for a highly entrepreneurial Principal Data Scientist with deep payments expertise to help define and build the next generation of intelligent payment products. This individual will combine hands-on data science, machine learning, payment-domain expertise, and strong product instincts to identify high-value opportunities hidden within corporate payments data and turn them into production-ready intelligence. This is not a traditional analytics or reporting role. The successful candidate will start with ambiguous business problems, explore data, form hypotheses, build models and signals, and partner with Product and Engineering to turn insights into scalable capabilities. The role offers near-greenfield opportunity and end-to-end ownership within Corporate Payments, with a focus on revenue growth and measurable business value. The initial focus includes exploring opportunities such as BIN routing optimization, where small improvements across high transaction volumes may create significant net benefit, as well as supplier onboarding, customer acquisition, and identifying suppliers that already accept card payments and could be connected to WEX customers. Over time, this work can help establish a trusted intelligence foundation for future AI agents and agentic payment experiences. What You’ll Do: Discover high-value payment opportunities by combining deep payments knowledge with exploratory data science and machine learning. Use corporate payments data to identify opportunities for revenue growth, improved payment economics, supplier onboarding, and new customer acquisition; identify additional data that could unlock further analysis. Build intelligent payment data products that move beyond dashboards to predictions, recommendations, optimization, and decisioning. Develop models and signals related to merchant and supplier behavior, payment acceptance, authorization performance, BIN/interchange optimization, payment routing, fraud/risk, and payment economics. Create connected intelligence across customers, suppliers, merchants, transactions, payment methods, networks, and other payment entities. Identify patterns in large-scale transaction data that can improve acceptance, payment performance, revenue, and margin; form hypotheses and prove value with rigorous analysis. Prototype quickly using Python, SQL, machine learning, and modern data platforms to demonstrate value before scaling solutions. Partner with Product and Engineering to productionize models, signals, and decisioning capabilities. Design experimentation and measurement frameworks to demonstrate the incremental business value of models and recommendations. Help define the semantic and contextual intelligence needed for future payments AI agents and agentic experiences. Act as a thought partner to Payments leadership, proactively identifying opportunities rather than waiting for fully defined requirements. Mentor data analysts and collaborate with data scientists across the broader organization, sharing practices and raising the technical and data-science bar. What You Bring: 10 years of relevant experience in data science or a related quantitative discipline, including hands-on work building production machine-learning, predictive, optimization, or decisioning solutions. Deep subject-matter expertise in payments, credit cards, payment transactions, or payment infrastructure. Strong domain understanding is essential; technical ability alone is not sufficient. Experience in several relevant areas such as authorization, acceptance, interchange, BINs, merchant behavior, payment routing, fraud/risk, transaction economics, settlement, or payment-network dynamics. Strong Python and SQL skills and experience working with large-scale transactional datasets. Able to move between exploratory analysis, feature engineering, modeling, experimentation, and productionization. Ability to understand not only what happened in payment data, but why it happened and what the business could do differently. A builder and entrepreneurial mindset: able to independently identify an opportunity, explore data, test a hypothesis, prototype a solution, and develop a business case without requiring a fully defined roadmap. Strong product instincts and judgment to distinguish technically interesting work from work that creates measurable business value. Ability to work directly with Product, Engineering, Commercial, Risk, and business leaders and to communicate clearly and succinctly with director- and VP-level stakeholders. Customer empathy, proactive collaboration and sharing of learnings, accountability to lead by example, and willingness to challenge existing approaches constructively. Established technical leadership and a collaborative approach to mentoring others while remaining open to learning from peers. Nice to Have: Advanced degree (master’s degree or above) in Data Science, Statistics, Applied Mathematics, Computer Science, Operations Research, Economics, or another quantitative discipline. Equivalent depth demonstrated through experience may be considered on a case by case basis. Experience with R and modern data platforms such as Snowflake or Databricks. Python and SQL are required; SAS alone is not sufficient. Experience at a major card network, card company, payment processor, transaction-focused fintech, or financial institution. Curiosity about GenAI and agentic systems and interest in designing intelligence that can ultimately be consumed by AI agents. Why This Role Matters Corporate Payments has an opportunity to unlock meaningful revenue and net benefit from data that has recently become available. This role will lead the discovery and validation of those opportunities, including payment-routing and interchange economics, supplier connectivity, and customer growth. The successful candidate will have the autonomy to build a portfolio of high-value work, influence business decisions with evidence, and help establish scalable intelligence capabilities for future payment products and agentic experiences. As the portfolio grows, there may be an opportunity to build a supporting team over time; direct people management is not part of the initial role, and continued individual-contributor growth is also supported. The base pay range represents the anticipated low and high end of the pay range for this position. Actual pay rates will vary and will be based on various factors, such as your qualifications, skills, competencies, and proficiency for the role. Base pay is one component of WEX's total compensation package. Most sales positions are eligible for commission under the terms of an applicable plan. Non-sales roles are typically eligible for a quarterly or annual bonus based on their role and applicable plan. WEX's comprehensive and market competitive benefits are designed to support your personal and professional well-being. Benefits include health, dental and vision insurances, retirement savings plan, paid time off, health savings account, flexible spending accounts, life insurance, disability insurance, tuition reimbursement, and more. For more information, check out the "About Us" section. Pay Range: $215,000.00 - $243,800.00

Requirements

Must-haves

About the Team/Role
Discover high-value payment opportunities by combining deep payments knowledge with exploratory data science and machine learning.
Use corporate payments data to identify opportunities for revenue growth, improved payment economics, supplier onboarding, and new customer acquisition; identify additional data that could unlock further analysis.
Build intelligent payment data products that move beyond dashboards to predictions, recommendations, optimization, and decisioning.
Develop models and signals related to merchant and supplier behavior, payment acceptance, authorization performance, BIN/interchange optimization, payment routing, fraud/risk, and payment economics.
Create connected intelligence across customers, suppliers, merchants, transactions, payment methods, networks, and other payment entities.
Identify patterns in large-scale transaction data that can improve acceptance, payment performance, revenue, and margin; form hypotheses and prove value with rigorous analysis.
Prototype quickly using Python, SQL, machine learning, and modern data platforms to demonstrate value before scaling solutions.
Partner with Product and Engineering to productionize models, signals, and decisioning capabilities.
Design experimentation and measurement frameworks to demonstrate the incremental business value of models and recommendations.
Help define the semantic and contextual intelligence needed for future payments AI agents and agentic experiences.
Act as a thought partner to Payments leadership, proactively identifying opportunities rather than waiting for fully defined requirements.
Mentor data analysts and collaborate with data scientists across the broader organization, sharing practices and raising the technical and data-science bar.
10 years of relevant experience in data science or a related quantitative discipline, including hands-on work building production machine-learning, predictive, optimization, or decisioning solutions.
Deep subject-matter expertise in payments, credit cards, payment transactions, or payment infrastructure. Strong domain understanding is essential; technical ability alone is not sufficient.
Experience in several relevant areas such as authorization, acceptance, interchange, BINs, merchant behavior, payment routing, fraud/risk, transaction economics, settlement, or payment-network dynamics.
Strong Python and SQL skills and experience working with large-scale transactional datasets. Able to move between exploratory analysis, feature engineering, modeling, experimentation, and productionization.
Ability to understand not only what happened in payment data, but why it happened and what the business could do differently.
A builder and entrepreneurial mindset: able to independently identify an opportunity, explore data, test a hypothesis, prototype a solution, and develop a business case without requiring a fully defined roadmap.
Strong product instincts and judgment to distinguish technically interesting work from work that creates measurable business value.
Ability to work directly with Product, Engineering, Commercial, Risk, and business leaders and to communicate clearly and succinctly with director- and VP-level stakeholders.

Nice-to-haves

Experience at a major card network, card company, payment processor, transaction-focused fintech, or financial institution.
Curiosity about GenAI and agentic systems and interest in designing intelligence that can ultimately be consumed by AI agents.
Why This Role Matters

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Keywords to mirror

machine learningaipythonsql

Platform: workday
Effort: long

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