Data & Risk Strategy Enablement

WEXWEXUS•Remote, OH

3d•$114,600 - $134,500

About The Position

The Data & Risk Strategy Enablement role is part of WEX’s Global Risk Solutions and Strategy group, which combines data science, risk expertise, technology, and data sources to develop solutions that support decision-making across the client lifecycle. The role works at the intersection of data, risk strategy, data science, and technology, partnering with data scientists, risk strategists, data engineers, technology teams, and business stakeholders to provide trusted, scalable, well-understood data and features for analytics, modeling, and risk decisioning. The role serves as a data and risk subject matter expert and a bridge between data consumers and data engineering teams. It develops understanding of source systems, data structures, business processes, and data flows; translates business and risk needs into data requirements; and promotes common definitions, data quality, and reusable analytical capabilities. It also supports the feature-store ecosystem as a primary liaison between Risk Solutions and Strategy and partner organizations, helping teams adopt, contribute to, and improve the feature store. The position partners on data pipeline design and optimization, investigates data issues, and works with governance and technology teams to improve lineage, ownership, documentation, and controls. The role is expected to identify scalable solutions, communicate risks and dependencies, and help ensure data and features are discoverable, documented, trusted, and suitable for production risk applications.

Requirements

At least 6 years of professional experience working with data, analytics, data science, risk, data engineering, or related disciplines.
Strong understanding of data ecosystems, including source systems, data warehouses or lakes, ETL/ELT pipelines, data transformations, and downstream analytical consumption.
Advanced SQL skills and experience working with large, complex datasets.
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 with feature stores, feature engineering platforms, or similar reusable data and 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.
Ability to operate independently, identify ambiguous problems, and drive solutions across organizational boundaries.
Strong analytical, problem-solving, and critical-thinking skills.

Nice To Haves

Experience in financial services or a complex data environment.
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 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.
Experience with AI tools such as Gemini, ChatGPT, or similar.

Responsibilities

Serve as a trusted data and risk subject matter expert for data scientists, risk strategists, engineers, and business stakeholders.
Develop a deep understanding of source systems, data structures, business processes, and data flows supporting credit, fraud, and other risk strategies.
Translate business and risk requirements into clear data requirements, including appropriate data sources, transformations, features, and analytical approaches.
Establish and promote common definitions for risk, customer, account, transaction, and performance measures.
Identify opportunities to improve the availability, usability, quality, and scalability of data for risk and data science teams.
Bridge data consumers and data engineering teams by prioritizing and communicating data requirements and technical needs.
Partner with data engineering teams to design, optimize, and improve pipelines for risk analytics, modeling, and strategy.
Identify pipeline bottlenecks, redundancies, data quality issues, and opportunities to improve efficiency and reliability.
Help establish scalable data patterns that reduce duplicated preparation and enable analytical teams to focus on higher-value work.
Participate in data architecture and pipeline change reviews to ensure downstream analytical and risk requirements are met.
Identify upstream data changes and source-system dependencies that could affect models, strategies, reporting, or monitoring.
Build relationships with source-system owners and downstream data consumers to improve transparency and accountability.
Serve as the primary liaison to external teams supporting or consuming the feature store, representing data science and risk strategy needs.
Support feature-store adoption across data science, risk, and partner organizations by identifying barriers and developing practical solutions.
Promote best practices for feature development, documentation, reuse, governance, and consumption.
Help maintain and monitor feature definitions, metadata, lineage, quality, ownership, and lifecycle management.
Identify opportunities to migrate duplicated or manually maintained features into reusable, governed feature-store assets.
Gather internal-user feedback and translate it into feature-store enhancements, capabilities, and priorities.
Promote reuse of existing features and reduce unnecessary duplication across models and analytical solutions.
Help ensure features are discoverable, well documented, trusted, and fit for production risk applications.
Establish a strong data-quality mindset for data used in 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.
Communicate data risks, dependencies, and limitations that could affect analytical or risk outcomes.

Benefits

Health insurance
Dental insurance
Vision insurance
Retirement savings plan
Paid time off
Paid volunteering time
Health savings account
Flexible spending accounts
Life insurance
Disability insurance
Tuition reimbursement
Quarterly or annual bonus eligibility for non-sales roles, based on role and applicable plan