Data Scientist
We’re building a universal data API that lets brokers, TMSs, fintechs, and fleets connect to truck and trailer data through a single integration. Catena sits beneath the freight ecosystem, normalizing real-time telematics and execution data so platforms can automate workflows, reduce risk, and make better decisions.
As a Data Scientist at Catena, you’ll focus on turning large-scale, messy logistics data into clear insights, proof points, and decision-ready outputs that power product direction, GTM motion, and customer conviction.
This is not a research-only or academic role. You’ll work directly with real production data from hundreds of thousands of trucks and trailers and collaborate closely with product, engineering, and go-to-market teams to show what’s possible with Catena’s data layer.
Role Summary
You’ll own the analysis, exploration, and synthesis of Catena’s data to support three core objectives:
- Prove value to TMSs, visibility platforms, brokers, and shippers
- Enable GTM with concrete demos, sandboxes, and ROI-driven examples
- Inform product strategy with real-world patterns from the network
You’ll help create large-scale sandboxes, identify patterns like capacity availability, lane behavior, dwell, and utilization, and translate those into narratives customers can immediately understand.
What You’ll Do
Insight Generation & Analysis
- Analyze large-scale telematics and execution data across fleets, lanes, and time
- Identify patterns in capacity, utilization, dwell, reliability, HOS, and asset behavior
- Develop metrics and summaries that reflect real-world freight performance
GTM Enablement
- Build and maintain large-scale sandboxes (1,000+ vehicles) using masked or synthetic data
- Create compelling examples for sales, pilots, and customer conversations
- Partner with GTM to turn raw data into clear ROI stories and proof points
Product & Platform Feedback
- Surface data-driven insights that influence roadmap priorities
- Validate assumptions about customer use cases with real network data
- Help define “decision-grade” metrics that customers actually trust
Cross-Functional Collaboration
- Work closely with product, engineering, and FDEs to understand data nuances
- Support pilots and strategic accounts (e.g., TMS, visibility, broker platforms)
- Translate technical findings into clear narratives for non-technical audiences
Data Quality & Modeling (Lightweight)
- Help define data quality checks, thresholds, and confidence measures
- Assist in shaping normalized views (lane history, asset identity, availability)
- Focus on interpretability and usability over black-box modeling
Skills & Qualifications
- Strong analytical foundation with experience in Python, SQL, and data analysis workflows
- Comfort working with large, messy, real-world datasets
- Ability to reason about operational systems using imperfect data
- Experience turning analysis into clear business insights and narratives
- Strong communication skills across technical and non-technical teams
- Comfortable working in ambiguity and early-stage environments
Ideal Candidate Profile
- 3–6+ years in data science, analytics, or applied research roles
- Experience in logistics, supply chain, marketplaces, or networked platforms is a big plus
- Excited about building examples and insight, not just models
- Enjoys working close to customers and real business problems
- Pragmatic, curious, and impact-driven
United States