Data & Risk Strategy Enablement

Job not on LinkedIn

🔥 51 minutes ago

🇺🇸 United States – Remote

💵 $114.6k - $134.5k / year

⏰ Full Time

🟡 Mid-level

🟠 Senior

🎲 Risk

🦅 H1B Visa Sponsor

This company has sponsored H1B visas in the past.

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WEX

WebsiteLinkedInAll Job Openings

5001 - 10000 employees

Founded 1983

🏥 Healthcare

📦 Logistics

✈️ Travel

💰 $310M Post-IPO Debt on 2020-06

Healthcare • Logistics • Travel

WEX is a global commerce platform specializing in various business solutions to address operational challenges. They provide services in managing and mobilizing fleets with their fuel card systems, offering comprehensive fleet management and analytics. Additionally, they focus on business payments solutions that streamline processes across industries, enhancing efficiency and security. WEX is also involved in employee benefits administration, helping organizations effectively manage health and reimbursement accounts. Their diverse range of services caters to numerous sectors, emphasizing innovation, sustainability, and effective solutions for business growth.

📋 Description

• Serve as a trusted data and risk subject matter expert for data scientists, risk strategists, engineers, and business stakeholders • Develop deep understanding of critical 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, identifying 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 data availability, usability, quality, and scalability • Bridge data consumers and data engineering teams by prioritizing and communicating data requirements and technical needs • Design, optimize, and improve pipelines supporting risk analytics, modeling, and strategy • Identify pipeline bottlenecks, redundancies, data quality issues, and efficiency and reliability improvements • Establish scalable data patterns that reduce duplicated data preparation • Participate in data architecture and pipeline change design and review • Identify upstream data changes and source-system dependencies affecting models, strategies, reporting, or monitoring • Build relationships with source-system owners and downstream data consumers • Serve as primary liaison between Risk Solutions and Strategy and the broader feature store ecosystem • Support feature-store adoption across data science, risk, and partner organizations • Establish best practices for feature development, documentation, reuse, governance, and consumption • Manage feature definitions, metadata, lineage, quality, ownership, and lifecycle management • Help migrate duplicated or manually maintained features into reusable, governed feature-store assets • Gather user feedback and translate it into feature-store enhancements and priorities • Promote feature reuse and reduce duplication across models and analytical solutions • Ensure features are discoverable, documented, trusted, and fit for production risk applications • Investigate and coordinate resolution of data issues across source systems, pipelines, and downstream consumers • Partner with data governance and technology teams to improve lineage, ownership, documentation, and controls • Identify gaps in data definitions and establish consistent business and technical definitions • Communicate data risks, dependencies, and limitations affecting analytical or risk outcomes • Build credibility and relationships across data engineering, technology, risk, data science, and source-system teams • Influence teams without direct authority and drive solutions across organizational boundaries • Identify reusable capabilities, common features, and scalable solutions

🎯 Requirements

• 6 or more years of professional experience working with data, analytics, data science, risk, data engineering, or related disciplines • Preferably experience 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 • 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 • 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 • 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

🏖️ Benefits

• Generous paid time off • Paid volunteering time • Health insurance • Dental insurance • Vision insurance • Retirement savings plan • Health savings account • Flexible spending accounts • Life insurance • Disability insurance • Tuition reimbursement • Quarterly or annual bonus eligibility for non-sales roles • Comprehensive and market competitive benefits • Diversity and inclusion culture

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