AI-Driven Insights and Lead Prediction for Cleantech Expansion

Company Global Cleantech Directory Connecting cleantech businesses globally to accelerate sustainability and innovation. Project Summary This project will support the Global Cleantech Directory platform by leveraging advanced data analytics and AI to track user engagement, forecast growth, and identify high-value leads. Interns will apply machine learning to real user behavior data and create predictive models and dashboards to help increase conversions and platform adoption. Their work will directly improve decision-making, marketing, and user experience design across the platform. Internship Objectives Build predictive models to understand user intent and likelihood of sign-up or upgrade Analyze user interaction data (listings viewed, chatbot messages, time on page, etc.) Create dashboards that summarize listing engagement, user trends, and regional growth Deliver strategic recommendations to improve business conversion and user retention Integrate data-driven insights with existing chatbot and AI assistant design Tools & Data Languages/Tools: Python, SQL, Tableau, Power BI, Excel Data Sources: Platform activity logs, listing metadata, chatbot transcripts (if anonymized) Collaboration: Slack or Teams, Zoom check-ins, shared Google Drive or Notion for documentation Suggested Skills Intermediate to advanced knowledge of Python (pandas, sklearn, matplotlib) Experience in classification models, clustering, or time series forecasting Familiarity with dashboard tools (Power BI, Tableau) Ability to explain findings to non-technical stakeholders Ideal Student Profile MDA student with strong curiosity and a business mindset Interest in cleantech, sustainability, or digital platforms Able to work independently while meeting key project milestones Timeline Duration: 10–12 weeks (Sept 22–Dec 14, 2025) Estimated hours: 350–480 hours Mode: Remote Why Join This Project? Help a cleantech platform grow faster using the power of AI. Your work will directly influence global business engagement, and you’ll leave with a real-world analytics portfolio that demonstrates high-impact, applied data science.

International Law Assessment for Foreign Expansion

Recently, our organization has begun discussing the possibility of expanding internationally. As we begin to assess the environment and develop a strategy, we want your help in evaluating the laws, and legal processes of properly expanding to new countries. Tasks may include: Identifying the key legal factors for expansion that we may be impacted by including local laws and regulations, taxes, employment, and exporting. Identifying barriers to entering a new country. Determining the best legal structure for our company and outlining the expansion process. Researching options for intellectual property rights and trademark protection of our products/services. Creating a framework for contracts involving the international sale of our goods. Compile a report including all of the above research and writing to be presented to the owner of the company.

Sustainability-Driven Marketing Strategy for Global Cleantech Directory

The Global Cleantech Directory aims to enhance its reputation as a leader in ecological responsibility by integrating green branding into its consumer and member engagement practices. This project focuses on designing marketing strategies and communication frameworks that emphasize sustainability. The objective is to develop outreach campaigns that highlight the Directory's commitment to ecological responsibility, thereby enhancing brand trust and fostering meaningful engagement with cleantech stakeholders worldwide. By positioning the platform as a leader in promoting responsible consumption and sustainable innovation, the project seeks to strengthen the Directory's role as a trusted platform in the cleantech industry. The project will involve analyzing current engagement practices, identifying opportunities for incorporating green branding, and creating a comprehensive strategy that aligns with the Directory's sustainability goals.

Data Framework for LED Optimization in Greenhouses and Vertical Farming

Lumesmart Inc. seeks to enhance its LED lighting solutions by leveraging AI to simulate performance across diverse greenhouse and vertical farming environments. The goal of this project is to design a comprehensive data collection and analysis framework that will serve as the foundational training input for an AI tool. This AI tool will be capable of simulating how different LED lighting solutions perform under various agronomic and operational conditions, ultimately generating ROI-focused recommendations for growers. Students will be tasked with identifying key data points necessary for accurate and actionable AI simulations. These data points may include environmental factors, crop types, growth stages, and energy consumption metrics. By applying classroom knowledge in data analysis and AI, students will contribute to creating a robust framework that supports Lumesmart Inc.'s mission to optimize LED lighting solutions for sustainable agriculture. Key Responsibilities Identify and document key data inputs (e.g., crop type, growth stage, planting density, fixture height, light spectrum, electricity rates). Recommend additional agronomic and environmental variables to improve AI-driven simulations. Develop a grower-facing input form to standardize data collection. Propose a mapping framework linking each input to outputs (fixture selection, energy use, yield, ROI). Design a reporting structure for how results can be automated into grower-friendly ROI reports once AI is integrated.