Senior Data Scientist

Job Description

Sr. Data Scientist, Risk Solutions

Our Team: The Global Risk Solutions and Strategy group is a fast-growing team optimizing risk solutions and models, and we are a key function to help enable WEX's strategic objectives. The Risk Solutions Team employs data science methodologies (machine learning and statistical frameworks), a wide suite of data types, and modern technologies to develop solutions to inform decision making. Our team helps the firm identify and measure credit and fraud risk to proactively manage the risk throughout the client's life-cycle. As such, you will not only be working with the latest data and machine learning technologies and algorithms, you will be working in a dynamic environment alongside our stakeholders and domain experts to build models and drive better decision-making.

Our Company: WEX is a fast-growing multinational payments company based in beautiful Portland, Maine. WEX headquarters is situated in the heart of Portland amongst some of the best restaurants in the country and overlooking the ocean and lighthouses. When you are here, you know you are in Maine. We have generous paid time off and paid volunteering time, not to mention great benefits and a culture that values diversity and inclusion.

You are a data-driven problem solver who thrives in identifying and mitigating fraud threats while leveraging data science methodologies to enhance risk management. You have a deep understanding of fraud typologies and enjoy leveraging advanced machine learning models to prevent fraud. You recognize where machine learning and data science fit within a broader fraud prevention and risk strategy and can assess when a problem is better addressed through product enhancements, rule-based systems, or operational interventions. You believe that communication and relationships are key to success alongside your data and machine learning prowess.

• Partner with stakeholders to understand fraud risk challenges and translate them into data-driven solutions to measure and monitor fraud risk across the firm's products and services.

• Leverage advanced machine learning, Artificial Intelligence, and statistical methods and technologies to design flexible, scalable, and automated risk modeling solutions.

• Develop code and automated processes to extract fraud patterns from large scale transactional data, device intelligence, behavioural analytics, and other risk indicators.Keep abreast with emerging trends in machine learning and identify opportunities to leverage new tools to solve problems and improve processes

• Synthesize findings into actionable insights and articulate them to the appropriate stakeholders.

• Collaborate closely with fraud strategy, risk operations, and technology teams to integrate fraud model solutions both in rule-based systems and cloud infrastructures.

• Mentor and support junior data scientists, sharing knowledge and best practices to elevate the data science practice at WEX.

• Proactively identify and communicate challenges, opportunities, and risks associated with project work to ensure timely completion of the entire product

How you'll engage:

• Insights Driven: Clear hypothesis and objective driven analytics that help drive our business decisions and ongoing metrics

• Stakeholder Aligned: Understand the needs and audience for deliverables with a succinct and tailored message to maximize impact

• Results Focused: Rigorous focus on how analytics drive the end to end experiences with clear path to production and measurable impact

• Dynamic Collaboration: Drive continual improvement of our team best practices and processes to power collaboration

• Quality Mindset: Trust in our findings is critical so data and analytic quality is understood and accounted for from the beginning

• Curiosity and Learning: Learn new technologies and collaborate and teach others how to use them as necessary.

• 3+ years of professional experience in data science, machine learning, or artificial, with a strong focus on fraud detection, including transaction and application monitoring in the fintech/ financial services industry.

• Master's or Ph.D. degree in a quantitative field such as Mathematics, Statistics, Data Science, Operations Research, Computer Science.

• Strong knowledge of fraud risk models, including supervised and unsupervised learning techniques, and behavioral analysis.

• 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, pandas, etc..

• Understanding of model deployment requirements for scalable solutions

• Deep expertise in statistical and machine learning techniques, including modeling, testing and inference, sampling methods, supervised and unsupervised learning.

• Strong communication and presentation skills with an ability to relate complex analytics findings to business outcomes

• Adaptable and comfortable working collaboratively and independently in a self-starting manner

• Evidence of creative problem solving, critical thinking and a continual learning mindset in fraud prevention.

• Prior experience building application and transaction fraud detection models.

• Knowledge of external fraud intelligence sources, including third-party data providers e.g. LexisNexis.

• Knowledge of fraud rules-based systems and how machine learning can complement rule-based decisioning.

• Experience using cloud environments to develop advanced models, such as AWS Sagemaker

• Experience with end-to-end machine learning systems and MLOps framework

• Experience with LexisNexis products

Data Science, Machine Learning, Statistical Learning, Artificial Intelligence, Fraud, Transaction Monitoring, MLOps, Behavioral Analytics