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About This Role
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
Salary Context
This $215K-$243K range is above the 75th percentile for Data Scientist roles in our dataset (median: $162K across 195 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 11,520 AI roles we're tracking, Data Scientist positions make up 4% of the market. At WEX Inc., this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $190,000 based on 1,564 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($229K) sits 21% above the category median. Disclosed range: $215K to $243K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
WEX Inc. AI Hiring
WEX Inc. has 2 open AI roles right now. They're hiring across Data Scientist, AI Product Manager. Positions span Remote, US, Portland, ME, US. Compensation range: $145K - $243K.
Remote Work Context
Remote AI roles pay a median of $188,200 across 2,430 positions. About 6% of all AI roles offer remote work.
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (140) are outnumbered by mid-level (5,630) and senior (4,176) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $266,050 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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