Sr Credit Data Scientist

WEXWEXUS•Remote, OH

1d•$140,000 - $165,400

About The Position

The Global Risk Solutions and Strategy group develops risk solutions and models to support WEX’s strategic objectives. The Risk Solutions Team applies data science methodologies, including machine learning and statistical frameworks, to varied data sources and modern technologies to identify and measure credit and fraud risk throughout the client lifecycle. The role works with stakeholders and domain experts to build models and improve decision-making. The position partners with stakeholders to understand fraud risk management requirements and turn them into data-driven solutions for measuring and monitoring fraud risk across products and services. It contributes to the full model development and deployment lifecycle, designing scalable and automated risk modeling solutions and extracting fraud risk patterns from large-scale application, transaction, behavioral, and other risk-indicator data. The role also keeps up with emerging machine-learning developments and mentors junior data scientists. Success in the role involves producing clear, objective-driven analytics that inform business decisions; tailoring communications to stakeholders; connecting analytics to production and measurable outcomes; improving team practices and collaboration; ensuring analytic quality; and learning and sharing new technologies.

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 financial services.
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 used in fraud risk management from external credit bureaus and internal risk management processes.
Advanced SQL knowledge and experience creating and managing large datasets to organize and extract useful information.
Advanced Python or R knowledge 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.
Ability to adapt and work collaboratively and independently in a self-starting manner.
Creative problem-solving, critical thinking, and a continual learning mindset in credit risk management.

Nice To Haves

Prior experience mentoring data scientists.
Prior roles 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.
Preferred experience with AI tools such as Gemini and 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 end-to-end model development and deployment lifecycle to support timely product completion.
Apply machine learning, artificial intelligence, and statistical methods to design flexible, scalable, and automated risk modeling solutions.
Develop and review code and automated processes to 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, sharing knowledge and best practices.
Create objective-driven analytics that inform business decisions and ongoing metrics.
Communicate findings in a way that is tailored to stakeholder needs and audience.
Connect analytics to end-to-end experiences, production, and measurable impact.
Improve team practices and processes to support collaboration.
Ensure data and analytic quality is understood and accounted for from the beginning.

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, based on role and applicable plan