[Hiring] Data & Risk Strategy Enablement @WEXWEXUS

Role Description

Our Team:

• The Global Risk Solutions and Strategy group is a fast-growing team optimizing risk solutions, models, and strategies.
• We enable WEX’s strategic objectives by combining data science, risk expertise, technology, and a wide range of data sources.
• Our team helps 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:

• Partner closely with data scientists, risk strategists, data engineers, technology teams, and business stakeholders.
• Ensure teams have trusted, scalable, well-understood data and features to support analytics, modeling, and risk decisioning.

Qualifications

• 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.

Requirements

• 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.
• Establish and promote common definitions for key risk, customer, account, transaction, and performance measures.
• Identify opportunities to improve the availability, usability, quality, and scalability of data.
• Act as a bridge between data consumers and data engineering teams.
• 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 efficiency and reliability of existing data pipelines.
• Help establish scalable data patterns that reduce duplicated data preparation.
• Participate in the design and review of data architecture and pipeline changes.
• 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.
• 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.
• Support the feature store 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.
• 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.

Benefits

• Health, dental, and vision insurances.
• Retirement savings plan.
• Paid time off.
• Health savings account.
• Flexible spending accounts.
• Life insurance.
• Disability insurance.
• Tuition reimbursement.
• Comprehensive and market competitive benefits designed to support personal and professional well-being.

Company Description

WEX is a fast-growing multinational payments company based in beautiful Portland, Maine. We have generous paid time off and paid volunteering time, not to mention great benefits and a culture that values diversity and inclusion.