Data Scientist

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:

1. Prove value to TMSs, visibility platforms, brokers, and shippers
2. Enable GTM with concrete demos, sandboxes, and ROI-driven examples
3. 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