Fraud / Credit 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, collections 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.

Who You Are

You are a data scientist who excels at identifying solutions with machine learning and artificial intelligence models. You effectively assess how to best address a problem, recognizing where Machine Learning and Artificial Intelligence fits within a broader strategy. Your belief in strong communication and relationships is key to success, alongside your data and machine learning prowess. You thrive in identifying and mitigating risks and opportunities for the business, and enjoy employing advanced machine learning models for prevention.

• Learn from stakeholders and leaders on how to connect a business problem to data-driven solutions to measure and monitor risk across the firm's products and services.

• Leverage a broad spectrum of advanced statistical and machine learning methods and technologies to design flexible, scalable, and automated modeling solutions.

• Develop code and automated processes to combine and transform large volumes of data from disparate sources, to extract informative patterns.

• 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.

• 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.

• 1 to 3 years of hands-on experience in data science, machine learning, or artificial intelligence, preferably in fintech/ financial services industry

• Excellent analytical, creative problem-solving, and critical thinking skills, with the ability to tackle complex challenges and deliver innovative solutions.

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

• Advanced knowledge of SQL and experience creating and managing large datasets to organize and extract useful information

• Working knowledge of Python or R and experience with data science libraries such as lightgbm, scikit-learn, pandas, numpy etc.

• 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

• Prior experience building machine learning risk models in payment processing

space

• Knowledge of data attributes and coverage of risk-factors for credit, fraud or other risk domains.

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

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

Data Science, Machine Learning, Statistical Learning, Artificial Intelligence, Credit Risk, Fraud, Finance, Collections, Optimization