Sr Credit Data Scientist

WEX•Maine, NY

3d•$140,000 - $165,400

About The Position

The Sr Credit Data Scientist will work within WEX’s Global Risk Solutions and Strategy group and Risk Solutions Team, which uses data science methodologies, varied data sources, and modern technologies to develop risk solutions and inform decision-making. The team identifies and measures credit and fraud risk to proactively manage risk throughout clients’ life cycles. This role partners with stakeholders to translate fraud risk management requirements into data-driven solutions for measuring and monitoring fraud risk across products and services. It applies machine learning, artificial intelligence, and statistical methods to build flexible, scalable, and automated risk models, and uses large-scale application, transaction, behavioral, and other risk data to identify fraud patterns. The role also contributes to the model development and deployment life cycle, keeps up with emerging tools and methods, and mentors junior data scientists. The position emphasizes hypothesis- and objective-driven analytics, stakeholder-aligned communication, production-ready results with measurable impact, collaborative improvement of team practices, high-quality analysis, and continued learning. WEX offers a comprehensive benefits package that includes health, dental, and vision insurance, a retirement savings plan, paid time off, health savings and flexible spending accounts, life and disability insurance, and tuition reimbursement. The stated pay range is $140,000–$165,400; actual pay varies based on qualifications and other factors. Non-sales roles are typically eligible for a quarterly or annual bonus, subject to role and plan.

Requirements

At least 4 years of professional experience in data science, machine learning, and artificial intelligence, focused on fraud risk management in underwriting, behavioral surveillance, and loss prevention in the financial services industry.
Master’s or Ph.D. degree in a quantitative field such as Mathematics, Statistics, Data Science, Operations Research, or Computer Science.
Strong knowledge of fraud risk drivers in small and medium-sized businesses, public firms, and private firms, including data typically used in fraud risk management from external credit bureaus and internal risk management processes.
Advanced knowledge of SQL and experience creating and managing large datasets to organize and extract useful information.
Advanced knowledge of Python or R and experience with common data science libraries such as LightGBM, scikit-learn, and pandas.
Deep understanding of model deployment requirements for scalable solutions and real-time feature stores.
Deep expertise in statistical and machine learning techniques, including modeling, testing and inference, sampling methods, and supervised and unsupervised learning.
Strong communication and presentation skills, including the ability to relate complex analytics findings to business outcomes.
Adaptability and ability to work collaboratively and independently in a self-starting manner.
Creative problem-solving, critical-thinking, and continual-learning mindset in credit risk management.

Nice To Haves

Prior experience mentoring data scientists.
Prior roles responsible for building underwriting and behavioral models.
Knowledge of external intelligence sources and third-party data providers such as LexisNexis, Socure, and Dun & Bradstreet.
Experience using cloud environments to develop advanced models, such as AWS SageMaker.
Experience with end-to-end machine learning systems and MLOps frameworks.
Experience with AI tools such as Gemini or ChatGPT.

Responsibilities

Partner with stakeholders to understand fraud risk management requirements and translate them into data-driven solutions to measure and monitor fraud risk across products and services.
Identify and communicate challenges, opportunities, and risks across the model development and deployment life cycle to support timely completion.
Use machine learning, artificial intelligence, and statistical methods and technologies to design flexible, scalable, and automated risk modeling solutions.
Develop and review code and automated processes that extract fraud risk patterns from large-scale application and transaction data, behavioral patterns, and other risk indicators.
Monitor emerging machine learning trends and identify opportunities to use new tools to solve problems and improve processes.
Mentor and support junior data scientists and share knowledge and best practices.
Produce objective-driven analytics that inform business decisions and ongoing metrics.
Tailor deliverables to stakeholders and audiences, clearly communicating findings and their business relevance.
Focus analytics on end-to-end experiences, production readiness, and measurable impact.
Improve team practices and processes to support collaboration, and ensure data and analytic quality is considered from the outset.
Learn new technologies and collaborate with and teach others as needed.

Benefits

Health insurance
Dental insurance
Vision insurance
Retirement savings plan
Paid time off
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