Data & Risk Strategy Enablement

WEX•Maine, NY

3d•$114,600 - $134,500

About The Position

The role is part of WEX’s Global Risk Solutions and Strategy group, which combines data science, risk expertise, technology, and diverse data sources to support credit and fraud risk management and data-driven risk strategies. The position 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 analytical users, business stakeholders, source-system owners, and data engineering teams. It translates business and risk needs into data requirements, improves data pipelines and quality, and helps establish consistent definitions, governance, and reusable data practices. A central focus is enabling and optimizing the feature store. The role represents risk and data science needs to partner organizations, supports adoption, promotes reusable and governed features, and helps ensure features are discoverable, documented, trusted, and suitable for production risk applications. The position also identifies data risks and dependencies, coordinates issue resolution, and promotes scalable improvements across the data ecosystem.

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 working with feature stores, feature engineering platforms, or similar reusable data or analytics infrastructure.
Ability to understand data science and machine-learning requirements, including feature development, evaluation, consumption, and operationalization.
Strong understanding of credit risk, fraud risk, payments, or other financial-services risk domains.
Strong communication and relationship-building skills, including the ability to influence teams without direct authority.
Ability to work 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 working where data science, risk strategy, and data engineering are highly interconnected.
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 consistent definitions for key risk, customer, account, transaction, and performance measures.
Identify opportunities to improve the availability, usability, quality, and scalability of data used by risk and data science teams.
Bridge data consumers and data engineering teams, prioritizing and communicating data requirements and technical needs.
Partner with data engineering teams to design, optimize, and improve pipelines supporting 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 data preparation.
Participate in reviews of data architecture and pipeline changes to ensure they meet downstream analytical and risk requirements.
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 between the Risk Solutions and Strategy organization and external teams supporting or consuming the feature store.
Support feature-store use across data science, risk, and partner organizations by identifying barriers and developing practical solutions.
Establish and promote best practices for feature development, documentation, reuse, governance, and consumption.
Partner with teams to 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 suitable 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.
Build credibility through knowledge of data, risk, source systems, and analytical users’ needs.
Communicate effectively across technical and non-technical audiences, connecting business and risk objectives with technical data requirements.
Enable data scientists, risk strategists, and partner teams to find, understand, trust, and use data.
Collaborate across data engineering, technology, risk, data science, and source-system teams to solve cross-functional problems.
Identify reusable capabilities, common features, scalable solutions, and opportunities for continuous improvement.
Develop and share knowledge of evolving data platforms, feature stores, source systems, and analytical technologies.

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, subject to role and applicable plan