Data Scientist Credit Risk

Motive empowers the people who run physical operations with tools to make their work safer, more productive, and more profitable. For the first time ever, safety, operations and finance teams can manage their drivers, vehicles, equipment, and fleet related spend in a single system. Combined with industry leading AI, the Motive platform gives you complete visibility and control, and significantly reduces manual workloads by automating and simplifying tasks.

Motive serves more than 120,000 customers – from Fortune 500 enterprises to small businesses – across a wide range of industries, including transportation and logistics, construction, energy, field service, manufacturing, agriculture, food and beverage, retail, and the public sector.

Visit gomotive.com to learn more.

We are looking for a Data Scientist to build the models that power the credit risk and fraud functions for the Motive Card, a high-priority business area for Motive. The Motive Card is a corporate card natively integrated with a fleet management platform, giving businesses an all-in-one solution to automate their financial and physical operations. As a member of our team you’ll help frame the problems, build models and products that win customers, and leverage machine learning at a massive scale to solidify Motive’s technology lead in the connected fleet management space.

What You’ll Do:

• Work closely with Risk, Product and Engineering teams to build, improve and implement underwriting and fraud models
• Derive insights from complex data sets to identify credit and fraud risk
• Apply statistical and machine learning techniques on large datasets
• Evaluate the utility of non-traditional data sources

What We’re Looking For:

• Bachelor's degree or higher in a quantitative field, e.g. Computer Science, Math, Economics, or Statistics
• 4+ years experience in data science, machine learning, and data analysis in an Enterprise environment
• Expertise in applied probability and statistics
• Experience building credit risk and fraud models
• Deep understanding of machine learning techniques and algorithms
• End-to-end deployment data-driven model deployment experience
• Expertise in data-oriented programming (e.g. SQL) and statistical programming (e.g., Python, R). PySpark experience is a plus