Who We Are

Our Team: The Risk Data Science team is part of the Global Risk Solutions and Strategy group. We are a fast-growing team optimizing risk solutions and models, and we are a key function to help enable WEX’s strategic objectives. The Risk Data Science team uses a variety of advanced methodologies (including machine learning and statistical frameworks), a wide suite of data types, and modern technologies to develop solutions to inform decision making. One of the areas of our team helps the firm identify and measure credit 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.

Who You Are

You are driven to do great work. You love learning new things and solving complex problems with data tools and algorithms. You believe that communication and relationships are key to success alongside your data and machine learning prowess.

What you’ll do:

• Partner with stakeholders to develop data-driven methods to measure and monitor credit risk across the firm’s products and services.  

• Leverage understanding of credit processes (including credit origination, portfolio monitoring, credit line assignment, loss forecasting, and others) to design flexible, scalable, and automated modeling solutions.  

• Utilize advanced statistical and machine learning methods and technologies to deliver best-in-class models to support risk decision making

• Develop code and automated processes to manipulate high volume, high dimensional data sources to extract informative patterns, perform exploratory analyses and engineer useful features

• Keep abreast with emerging trends in lending, macroeconomic environment, and machine learning to identify new opportunities and tools to solve problems and drive innovation

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

• 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 data science 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. 
  
  Experience You’ll Bring:

• 3+ years of hands-on experience leveraging statistical and machine learning methods to deliver impactful solutions for measuring and managing credit risk, credit risk strategy, or other elements of the credit risk life-cycle

• Solid understanding of credit risk-drivers in small and medium sized businesses, public firms, and private firms, including data typically used in credit risk management from external credit bureaus and internal risk management processes 

• Master’s or PhD degree in a quantitative field such as Mathematics, Statistics, Data Science, Operations Research, Computer Science

• Advanced knowledge of SQL and experience creating complex processes to transform large datasets for modeling and analysis

• Solid knowledge of scripting languages such as Python or R and standard data visualization and modeling libraries such as scikit-learn, matplotlib, pandas, numpy etc.

• Solid knowledge of statistical principles, such as testing, probability distributions, Bayesian inference, maximum likelihood estimation, sampling, regression modeling, time series forecasting etc.

• Solid knowledge of machine learning algorithms such as ran