Data Scientist, Credit Risk
United States - Remote
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.
Visit gomotive.com to learn more.
About the Role:
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
Pay Transparency
Your compensation may be based on several factors, including education, work experience, and certifications. For certain roles, total compensation may include restricted stock units. Motive offers benefits including health, pharmacy, optical and dental care benefits, paid time off, sick time off, short term and long term disability coverage, life insurance as well as 401k contribution (all benefits are subject to eligibility requirements). Learn more about our benefits by visiting Motive Perks & Benefits.
The compensation range for this position will depend on where you reside. For this role, the compensation range is:
Creating a diverse and inclusive workplace is one of Motive's core values. We are an equal opportunity employer and welcome people of different backgrounds, experiences, abilities and perspectives.
Please review our Candidate Privacy Notice here.
The applicant must be authorized to receive and access those commodities and technologies controlled under U.S. Export Administration Regulations. It is Motive's policy to require that employees be authorized to receive access to Motive products and technology.
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Benefits/Perks401(k) contribution Dental Dental Care Disability coverage Diverse and inclusive workplace Health Health benefits Life Insurance Optical Optical and Dental Care Optical and dental care benefits Paid Time Off Pharmacy Restricted Stock Units Short term and long term disability coverage Sick time off
Tasks- Apply statistical techniques
- Build credit risk and fraud models
- Data Analysis
- Derive insights from data
- Evaluate non-traditional data sources
AI Applied probability Credit Risk Models Data analysis Data-driven Data-oriented programming Data Science Engineering Fleet Management Fraud models Logistics Machine Learning Manufacturing Probability PySpark Python R SQL Statistics Underwriting
Experience4 years
EducationBachelor's Bachelor's degree Business Computer Science Economics Engineering Finance Higher Master's degree Math Statistics
TimezonesAmerica/Anchorage America/Chicago America/Denver America/Los_Angeles America/New_York Pacific/Honolulu UTC-10 UTC-5 UTC-6 UTC-7 UTC-8 UTC-9