GenLogs
Data Scientist
Remote (United States) · Full-time
Sponsorship not specifiedDetected 223 days ago
AlgorithmsMachine LearningData ScienceComputer VisionLogistics
About the role
- By operating at the intersection of edge sensing, computer vision, AI-driven analytics, and large-scale field deployment, GenLogs is transforming how transportation data is captured, secured, and commercialized.
- ABOUT THE DATA TEAM The Data Science team at GenLogs transforms raw observational data from the Trident sensor network into high-value intelligence used by law-enforcement agencies, regulators, ports, and private-sector freight operators.
- ABOUT THE JOB You're a problem solver at heart.
Responsibilities
- GenLogs is a transportation-technology company building the next generation of truck intelligence.
- Through a nationwide network of sensors and proprietary data, we deliver real-time, high-fidelity insights into freight movement for commercial supply-chain customers and public-sector agencies.
- We partner closely with Engineering and Product to deploy algorithms at scale and with Go-to-Market teams to define customer-facing analyses that drive real operational outcomes.
- The team blends statistical rigor, ML capability, and domain expertise to create a new standard for freight intelligence in the United States.
- You ask great questions, validate assumptions with data, and are relentless in your pursuit of signal over noise.
Compensation
- Salary bands are benchmarked against high-growth technology companies and adjusted for market conditions.
Benefits
- You thrive at the intersection of engineering, math, and machine learning, and you're motivated by questions that don't have obvious answers.
- Equity grants are included in most full-time offers to ensure every team member participates in the company's long-term value creation.
Company info
- Our mission is to strengthen America's logistics backbone, combat freight fraud and cargo theft, and provide near-instantaneous visibility into commercial motor vehicle activity across major freight corridors.
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