Risk Data Scientist - Risk Solutions

About the Team/Role 

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 team uses advanced methodologies (machine learning and statistical frameworks), a wide suite of data types, and modern technologies to develop solutions for risk decision making. 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. 

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.

How you'll make an impact

• Partner with stakeholders to translate needs into data-driven solutions to manage risk across the firm’s products and services.
• Leverage a spectrum of advanced machine learning and statistical methods to design flexible, scalable, and automated modeling solutions.  
• Develop code and automated processes to manage large volumes of data from disparate sources and 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.

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. 

Experience You’ll Bring:

• 2+ years of hands-on experience in data science, machine learning, or artificial intelligence, preferably in fintech/ financial services industry
• Excellent 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
• Advanced knowledge of Python or R and experience with data science libraries such as lightgbm, scikit-learn, pandas, numpy etc.
• Expertise in the machine learning model lifecycle, testing and inference, sampling methods, and supervised learning techniques.
• 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

How you will stand out:

• Previous experience in risk models in collections, loss given default and loss modeling, and payments processing.
• Working knowledge of data attributes and coverage of risk-factors in external bureau data for small business.
• Experience using cloud environments to develop advanced models, such as AWS Sagemaker 
• Experience with modern model deployment techniques

Key Words

Data Science, Machine Learning, Artificial Intelligence, Credit Risk, Collections, Loss Forecasting, Loss Given Default,