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Principal Machine Learning Scientist
DIESEL LAPTOPS LLC
Remote, USA
Full-time
Senior Level
Remote
USD 118-126 hourly
Discovered 1 weeks ago
Free
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The Principal Data Scientist, Vehicle Analytics, is a senior individual contributor responsible for solving complex business, vehicle, and engineering problems through statistical analysis, experimentation, predictive modeling, and applied data science.
This role partners closely with Software Engineering, Data Engineering, Product, Remote Solutions Engineering, and customer-facing teams to identify high-value problems, define analytical approaches, validate hypotheses, and deploy reliable data-driven capabilities.
Machine learning is an important part of the role, but success is defined by selecting the most appropriate analytical method for each problem rather than applying machine learning where a simpler statistical or analytical approach would be more reliable, explainable, or useful.
This position does not have routine direct reports but is expected to provide scientific leadership, technical mentorship, and guidance across the organization.
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Company Overview
Diesel Laptops is a leading provider of diagnostic tools, repair information, software, training, and technology solutions for the commercial truck and off-highway vehicle repair industry.
We help repair facilities, fleets, technicians, and industry partners reduce downtime, improve repair accuracy, and make better operational decisions.
We are seeking a Principal Data Scientist, Vehicle Analytics, to help transform large volumes of vehicle telemetry, service history, repair outcomes, and operational data into meaningful insights, predictive capabilities, and production-ready analytical solutions.
Position Summary
The Principal Data Scientist, Vehicle Analytics, is a senior individual contributor responsible for solving complex business, vehicle, and engineering problems through statistical analysis, experimentation, predictive modeling, and applied data science.
This role partners closely with Software Engineering, Data Engineering, Product, Remote Solutions Engineering, and customer-facing teams to identify high-value problems, define analytical approaches, validate hypotheses, and deploy reliable data-driven capabilities.
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