SFC

Technical Specialist, Fleet Electrification North America (USA)

Remote - United States of America

Sponsorship not specifiedDetected 51 days ago

PythonSQLData AnalysisData VisualizationStatisticsAccountingExcelLogisticsProcurementGISCommunicationCollaborationPublic Speaking

About the role

Job description PLEASE NOTE: We're only considering candidates who are already based and registered in the USA.
Unfortunately, we can't offer visa sponsorship or help with relocation.
Smart Freight Centre (SFC) is a globally active non-profit organization dedicated to reducing greenhouse gas emissions from freight transportation through collaboration, transparency, and innovation.

Data Processing, Analytics & Modeling Gather, clean, validate, and organize operational, fleet, route, and infrastructure data across multiple projects.
Develop, maintain, and improve total cost of ownership (TCO), emissions, operational, and economic models to support fleet electrification decision-making.
Develop and automate analytical workflows using Python to improve efficiency, consistency, and scalability.
Update and maintain key model inputs including energy prices, incentives, emissions factors, vehicle performance assumptions, and infrastructure costs.
Identify inefficiencies in current analytical processes and implement practical improvements to increase scalability and repeatability.
Maintain clear documentation of data sources, assumptions, methodologies, and analytical approaches.
Support the transition of existing Excel-based analytical tools and processes into more scalable Python-based solutions where appropriate.
Develop visual outputs including dashboards, maps, charts, tables, and presentations.
Develop presentations and technical materials to communicate findings and recommendations effectively.
Project Delivery & Stakeholder Support Work closely with project managers and technical experts to integrate analytical insights into project delivery.

Occasional domestic and international travel may be required to support project activities, stakeholder engagement, workshops, and industry events.

Experience with fleet electrification, electric vehicles, charging infrastructure, energy systems, or freight transportation.
Experience with Power BI, Tableau, or similar business intelligence tools.
Experience with GIS and spatial analysis tools.
Experience working with SQL databases and large datasets.
Familiarity with logistics operations, trucking fleets, or transportation planning.
Experience leveraging automation, AI-assisted analysis, or advanced analytical tools to improve data workflows.
Master's degree in a relevant field.

Delivered high-quality TCO, emissions, and operational analyses supporting multiple fleet electrification projects across North America.
Improved the consistency, efficiency, and automation of analytical workflows across FEC initiatives.
Developed scalable tools, datasets, and analytical resources that reduce manual effort and improve project delivery.
Contributed directly to fleet electrification decisions made by leading shippers, carriers, and logistics providers.
Established yourself as a trusted technical resource within the Fleet Electrification Coalition and broader stakeholder ecosystem.
Strong analytical and quantitative skills, including economic, operational, or emissions analysis.
Proficiency in Python for data processing, analysis, modeling, and workflow automation.
Advanced proficiency in Microsoft Excel.
Experience creating data visualizations, dashboards, and analytical reports.
Strong communication skills, with the ability to explain technical concepts to diverse audiences.

Full access to OpenUp means you can connect with psychologists, join mindfulness classes, and explore courses to support your mental health.

Because remote doesn't mean working alone.
Join a global team built on trust, collaboration, and shared learning. ]]>
Support reporting and analytical deliverables for customers, partners, funders, and stakeholders.

This listing is sourced directly from SFC's careers page and normalized into a canonical job model.