Staff Data Scientist
Who we are:
Motive builds technology to improve the safety, productivity, and profitability of businesses that power the physical economy. Motive combines IoT hardware with AI-powered applications to connect and automate physical operations. Motive is one of the fastest-growing software companies in the world, serving more than 120,000 businesses, across a wide range of industries including trucking and logistics, construction, oil and gas, food and beverage, field service, agriculture, passenger transit, and delivery.
Motive is built on four foundational attributes; Own It, Less but Better, Build Trust, and Unlock Potential. This has taken our company to great heights, including being recognized by Fortune for Best Workplaces, Forbes Best Startup Employers, and Comparably for our Best Global Culture, Sales Team, Leadership Team, Career Growth, and CEO for Diversity. We’re proud to receive an employee net promoter score of 63 (according to Comparably) which places Motive in the top 5% of companies with 4,000 employees or more.
Today, our team is made up of more than 3,000 employees, located across the world, providing support to a wide range of customers. While most of our employees are remote, many have the opportunity to work on-site at any of our 8 global office locations. Visit our careers website to learn more about opportunities at Motive.
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. 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
- 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
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. Motive uses three geographic zones to determine pay range. For this role, the compensation ranges are:
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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