Clean Transportation Intern - Remote, US

Energy Solutions Internships

Location
Remote
Employment type
internship
Posted
Posted
  • Distributed Energy Resources

About the role

Interested in joining a growing company where you will work with talented colleagues, enhance a supportive and energetic culture, and be part of the climate solution? At Energy Solutions, we focus on the big impacts. And we believe that market-based programs can be a powerful force to deliver large-scale energy, carbon, and water-use savings. Since 1995, we’ve harnessed that power to offer proven, performance-based solutions for our utility, government, and institutional customers. Does data and eco-friendly transportation drive you... sometimes literally? Are you excited about the economic and mathematic side of real-world incentive programs? The Clean Transportation Intern will have great opportunities for a numbers-oriented student looking to gain practical program insights and experience. In this full-time (~40 hours/week) role, you will create a GIS—that's Geographic Information Systems—map which illustrates the combined impacts of our clean transportation incentive programs. The goal is to map our existing dealership relationships in proximity to customers, display the GHG emissions reductions for transitioning to zero carbon transportation modes, and develop a white paper comparing program trends to state and local policy goals. To complement this project, you will deep-dive peer-reviewed studies to build an analysis on the impacts of incentives on vehicle purchases. You’ll gain a deep understanding of complex programs by speaking with program managers and using program data to test theories, forecast economic behavior, measure economic relationships, and model the impact of our incentives against a base case. This experience will grant you: Hands-on practice using data modeling and visualization tools Knowledge of the CA transportation electrification landscape Daily collaboration with team members to support and understand program design decisions Your work will result in: Measurable data on program impacts that can be shared with clients and support business development activities Informative project deliverables that may help shape program design decisions To enable you for success, we are seeking candidates with these qualifications: Intermediate Microsoft Excel skills Skill in Python and/or R Currently enrolled or previously completed coursework in an economics or mathematics program Strong research, writing, and analytical skills While not required, we love to see candidates with: An interest in renewable energy, clean transportation, and/or incentive program design Experience with ArcGIS or a similar tool Compensation: Undergraduate student: $21/hr Graduate student: $24/hr In order to be eligible for this position, you must be: Currently enrolled full- or part-time in an undergraduate program as a second to fourth-year student or enrolled in a graduate program Please note we are unable to accept applications from first-year undergraduate students or recent graduates who are not enrolled in a program A US Citizen or permanent resident Please note we are unable to host international students Available to meet your weekly hours requirement between May and August for Summer and between September and November for Fall Motivated by the Energy Solutions’ mission to reduce carbon emissions with an equity lens You may apply to up to 2 positions per cycle. To apply, please submit: Your updated resume/CV. 2 short essays that meets the criteria below. A work sample that meets the criteria below. Your short essays must: Be original documents written by you. We will not accept work written by or with the assistance of ChatGPT or other AI/chatbot programs. Be 500 words or less each. Answer the following prompts: Describe how you have overcome adversity and applied the lessons learned to your academic, professional, or personal life. Describe how the internship you apply for will enhance or help launch your career in an energy related industry. Your work sample must: <li st

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