Senior Data Scientist

November 5

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Motive

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Electronic Logging Devices • Fleet Management • Vehicle GPS Tracking • Driver Vehicle Inspection Reports • IFTA Reporting

1001 - 5000

Description

• Who we are: • 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. • About the Role: • We are looking for a Staff Data Scientist to build the models that power the credit risk and fraud functions for the Motive Card, a key new focus area for Motive. • 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. • Responsibilities: • 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

Requirements

• Bachelor's degree or higher in a quantitative field, e.g. Computer Science, Math, Economics, or Statistics • 7+ years experience in data science, machine learning, and data analysis - specifically in the Credit Risk space • 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 big plus