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Data Scientist
JOB SUMMARY
Roles
Job details
Data ScientistWe’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 eco
System
, 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 SummaryYou’ll own the analysis, exploration, and synthesis of Catena’s data to support three core objectives:Prove value to TMSs, visibility platforms, brokers, and shippersEnable GTM with concrete demos, sandboxes, and ROI-driven examplesInform product strategy with real-world patterns from the networkYou’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 DoInsight Generation AnalysisAnalyze large-scale telematics and execution data across fleets, lanes, and timeIdentify patterns in capacity, utilization, dwell, reliability, HOS, and asset behaviorDevelop metrics and summaries that reflect real-world freight performanceGTM EnablementBuild and maintain large-scale sandboxes (1,000+ vehicles) using masked or synthetic dataCreate compelling examples for sales, pilots, and customer conversationsPartner with GTM to turn raw data into clear ROI stories and proof points
Product
Platform FeedbackSurface data-driven insights that influence roadmap prioritiesValidate assumptions about customer use cases with real network dataHelp define “decision-grade” metrics that customers actually trustCross-Functional CollaborationWork closely with product, engineering, and FDEs to understand data nuancesSupport pilots and strategic accounts (e. g. , TMS, visibility, broker platforms)Translate technical findings into clear narratives for non-technical audiencesData Quality Modeling (Lightweight)Help define data quality checks, thresholds, and confidence measuresAssist in shaping normalized views (lane history, asset identity, availability)Focus on interpretability and usability over black-box modelingSkills
Qualifications
Strong analytical foundation with experience in Python, SQL, and data analysis workflowsComfort working with large, messy, real-world datasetsAbility to reason about operational systems using imperfect dataExperience turning analysis into clear business insights and narrativesStrong communication skills across technical and non-technical teamsComfortable working in ambiguity and early-stage environmentsIdeal Candidate Profile3–6+ years in data science, analytics, or applied research rolesExperience in logistics, supply chain, marketplaces, or networked platforms is a big plusExcited about building examples and insight, not just modelsEnjoys working close to customers and real business problemsPragmatic, curious, and impact-driven.
System
, 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 SummaryYou’ll own the analysis, exploration, and synthesis of Catena’s data to support three core objectives:Prove value to TMSs, visibility platforms, brokers, and shippersEnable GTM with concrete demos, sandboxes, and ROI-driven examplesInform product strategy with real-world patterns from the networkYou’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 DoInsight Generation AnalysisAnalyze large-scale telematics and execution data across fleets, lanes, and timeIdentify patterns in capacity, utilization, dwell, reliability, HOS, and asset behaviorDevelop metrics and summaries that reflect real-world freight performanceGTM EnablementBuild and maintain large-scale sandboxes (1,000+ vehicles) using masked or synthetic dataCreate compelling examples for sales, pilots, and customer conversationsPartner with GTM to turn raw data into clear ROI stories and proof points
Product
Platform FeedbackSurface data-driven insights that influence roadmap prioritiesValidate assumptions about customer use cases with real network dataHelp define “decision-grade” metrics that customers actually trustCross-Functional CollaborationWork closely with product, engineering, and FDEs to understand data nuancesSupport pilots and strategic accounts (e. g. , TMS, visibility, broker platforms)Translate technical findings into clear narratives for non-technical audiencesData Quality Modeling (Lightweight)Help define data quality checks, thresholds, and confidence measuresAssist in shaping normalized views (lane history, asset identity, availability)Focus on interpretability and usability over black-box modelingSkills
Qualifications
Strong analytical foundation with experience in Python, SQL, and data analysis workflowsComfort working with large, messy, real-world datasetsAbility to reason about operational systems using imperfect dataExperience turning analysis into clear business insights and narrativesStrong communication skills across technical and non-technical teamsComfortable working in ambiguity and early-stage environmentsIdeal Candidate Profile3–6+ years in data science, analytics, or applied research rolesExperience in logistics, supply chain, marketplaces, or networked platforms is a big plusExcited about building examples and insight, not just modelsEnjoys working close to customers and real business problemsPragmatic, curious, and impact-driven.
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Catena Clearing Remote
