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WEX Inc

Data & Risk Strategy Enablement

Remote, United States

Pay Range: $114,600.00 - $134,500.00

Apply on wexinc.wd5.myworkdayjobs.com About employer

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Job details

Pay: Pay Range: $114,600.00 - $134,500.00
Posting language: English
Posted: Nov 12, 2026

Original job description

About the Team/Role Our Team: The Global Risk Solutions and Strategy group is a fast-growing team optimizing risk solutions, models, and strategies, and we are a key function in enabling WEX’s strategic objectives. The Team combines data science, risk expertise, technology, and a wide range of data sources to develop solutions that inform decision-making across the client lifecycle. Our team helps the firm identify, measure, and manage credit and fraud risk while enabling scalable, data-driven risk strategies.

As a member of the team, you will work at the intersection of data, risk strategy, data science, and technology. You will partner closely with data scientists, risk strategists, data engineers, technology teams, and business stakeholders to ensure our teams have trusted, scalable, well-understood data and features to support analytics, modeling, and risk decisioning.

Our Company: WEX is a fast-growing multinational payments company based in beautiful Portland, Maine. WEX headquarters is situated in the heart of Portland amongst some of the best restaurants in the country and overlooking the ocean and lighthouses. When you are here, you know you are in Maine. We have generous paid time off and paid volunteering time, not to mention great benefits and a culture that values diversity and inclusion.

Who You Are You are a data and risk subject matter expert who understands how data moves through complex source systems and how it ultimately needs to support analytics, data science, and risk decisioning. You are naturally curious about how data is generated, transformed, and consumed, and you are skilled at connecting technical teams with the business and risk context behind the data. You thrive in environments where data is distributed across multiple systems and teams, and where creating clarity, consistency, and trust is essential. You are comfortable challenging assumptions, establishing common definitions, identifying data quality issues, and working with data engineering teams to develop scalable solutions. You are also passionate about enablement and adoption. You see the feature store not simply as a technology platform, but as a critical capability for scaling data science and risk strategy. You will serve as the primary liaison between our team and partner organizations, helping them understand, adopt, and contribute to the feature store while ensuring that its capabilities evolve to meet the needs of data science and risk.

How you'll make an impact

Data & Risk Enablement

Serve as a trusted data and risk subject matter expert for data scientists, risk strategists, engineers, and business stakeholders.
Develop a deep understanding of critical source systems, data structures, business processes, and data flows that support credit, fraud, and other risk strategies.
Translate business and risk requirements into clear data requirements, helping teams identify the right data sources, transformations, features, and analytical approaches.
Establish and promote common definitions for key risk, customer, account, transaction, and performance measures to improve consistency across analytics and decisioning.
Identify opportunities to improve the availability, usability, quality, and scalability of data used by risk and data science teams.
Act as a bridge between data consumers and data engineering teams, helping prioritize and communicate data requirements and technical needs.

Data Pipeline Optimization

Partner closely with data engineering teams to design, optimize, and improve pipelines supporting risk analytics, modeling, and strategy.
Identify bottlenecks, redundancies, data quality issues, and opportunities to improve the efficiency and reliability of existing data pipelines.
Help establish scalable data patterns that reduce duplicated data preparation and enable data scientists and risk strategists to focus on higher-value analytical work.
Participate in the design and review of data architecture and pipeline changes to ensure they meet downstream analytical and risk requirements.
Proactively identify upstream data changes or source-system dependencies that could impact models, strategies, reporting, or monitoring.
Develop strong relationships with source-system owners and downstream data consumers to improve transparency and accountability across the data ecosystem.

Feature Store Optimization

Translate and support the relationship between the Risk Solutions and Strategy organization and the broader feature store ecosystem.
Serve as the primary liaison to external teams supporting or consuming the feature store, representing the needs of data science and risk strategy.
Support the feature store across data science, risk, and partner organizations by identifying barriers and creating practical solutions.
Establish and promote best practices for feature development, documentation, reuse, governance, and consumption.
Partner with teams to retain and monitor feature definitions, metadata, lineage, quality, ownership, and lifecycle management.
Work with data scientists and engineers to identify opportunities to migrate duplicated or manually maintained features into reusable, governed feature-store assets.
Gather feedback from internal users and translate it into enhancements, capabilities, and priorities for the feature store.
Promote reuse of existing features and reduce unnecessary duplication across models and analytical solutions.
Help ensure that features are discoverable, well documented, trusted, and fit for use in production risk applications.

Data Quality & Governance

Establish a strong quality mindset around data used for risk decisioning and model development.
Investigate and coordinate resolution of data issues across source systems, pipelines, and downstream consumers.
Partner with data governance and technology teams to improve data lineage, ownership, documentation, and controls.
Identify gaps in data definitions and work across teams to establish consistent business and technical definitions.
Proactively communicate data risks, dependencies, and limitations that could affect analytical or risk outcomes.

How You’ll Engage

Trusted Partner: Build credibility through deep knowledge of data, risk, source systems, and the needs of analytical users.
Data Translator: Connect business and risk objectives with technical data requirements and communicate effectively across technical and non-technical audiences.
Enablement Mindset: Make it easier for data scientists, risk strategists, and partner teams to find, understand, trust, and use data.
Enterprise Collaboration: Build strong relationships across data engineering, technology, risk, data science, and source-system teams to solve problems that cross organizational boundaries.
Quality Mindset: Treat data quality, lineage, definitions, and reliability as foundational to trusted risk decisions.
Scalable Thinking: Look beyond individual data requests to identify reusable capabilities, common features, and scalable solutions.
Continuous Improvement: Challenge inefficient processes and identify opportunities to improve how data is created, managed, and consumed.
Curiosity & Learning: Develop a deep understanding of evolving data platforms, feature stores, source systems, and analytical technologies and share that knowledge with others.

Experience You’ll Bring

6 or more years of professional experience working with data, analytics, data science, risk, data engineering, or related disciplines, preferably within financial services or a complex data environment.
Strong understanding of data ecosystems, including source systems, data warehouses/lakes, ETL/ELT pipelines, data transformations, and downstream analytical consumption.
Advanced SQL skills and experience working with large, complex datasets.
Demonstrated ability to understand complex source systems and translate business processes into data requirements and analytical datasets.
Experience partnering with data engineering teams to build, optimize, and troubleshoot data pipelines.
Strong understanding of data quality principles, data lineage, metadata, data governance, and data ownership.
Experience working with feature stores, feature engineering platforms, or similar reusable data/analytics infrastructure.
Ability to understand data science and machine learning requirements, including how features are developed, evaluated, consumed, and operationalized.
Strong understanding of credit risk, fraud risk, payments, or other financial services risk domains.
Strong communication and relationship-building skills, with the ability to influence teams without direct authority.
Demonstrated ability to operate independently, identify ambiguous problems, and drive solutions across organizational boundaries.
Strong analytical, problem-solving, and critical-thinking skills.
Experience serving as a data or analytics subject matter expert across multiple teams or organizational functions.
Experience owning or helping establish a feature store, feature platform, or enterprise analytics data product.
Experience partnering on shared data platforms or capabilities across data science and engineering organizations.
Experience developing enterprise data definitions, data standards, or data governance practices.
Experience working with cloud data platforms and modern data architectures.
Experience with data cataloging, lineage, data observability, or data quality platforms.
Experience working in an environment where data science, risk strategy, and data engineering operate as highly interconnected functions.
Experience with credit risk modeling, portfolio management, underwriting, fraud, collections, or other financial risk applications.
Experience influencing technical roadmaps and priorities across teams without direct management authority.
Demonstrated ability to simplify complex data environments and create reusable solutions that accelerate data science and risk strategy.
AI -- Gemini, ChatGPT or Similar

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: $114,600.00 - $134,500.00

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Jobs in the United States