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Internship

Digitization Intern

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Nexus Analytica

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Spearheaded the automation of data acquisition pipelines for E-mobility market analysis, developing robust Total Cost of Ownership (TCO) models and multi-variable sensitivity analyses to drive strategic decision-making in the transition to electric mobility.

Core Competencies & Stack

Python (Selenium, BeautifulSoup) Data Automation Sensitivity Analysis Total Cost of Ownership (TCO) Market Modeling Industrial Engineering
Tenure / Period 2023
Classification Internship
Skills Applied 6 Competencies

Work Summary

During my tenure at Nexus Analytica, I served as a key contributor to the Nexus EVolve and Nexus Charge ecosystems. My role operated at the critical intersection of industrial engineering and data science: transforming fragmented, manual data collection processes into automated, scalable pipelines. By architecting Python-based web scraping protocols and developing complex multi-variable sensitivity models, I enabled the firm to provide high-precision forecasting for EV adoption, infrastructure requirements, and fleet transition strategies for international stakeholders, including the UNDP and various national energy regulators.

Key Responsibilities & Core Systems

I was integrated into the core engineering team to support the development of Nexus EVolve, a sophisticated modeling tool designed to forecast the trajectory of electric vehicle adoption. My primary responsibilities included:

  • Automated Data Acquisition: Engineering robust scrapers to aggregate multi-source data regarding EV specifications and pricing.
This donut chart illustrates the distribution of vehicle data across various sources, highlighting the diversity of inputs integrated into the automated scraping pipeline.

  • TCO Module Development: Constructing a standardized “Total Cost of Ownership” (TCO) framework to facilitate comparative analysis between Internal Combustion Engine (ICE) vehicles and EV alternatives.

  • Market Analysis: Supporting the development of strategic roadmaps for regional markets, with a specific focus on identifying infrastructure gaps and quantifying policy impacts.

Engineering Initiatives & System Optimization

To optimize internal workflows, I applied Lean methodology principles to eliminate “waste” (Muda) within the data acquisition phase.

  • Process Standardization: I replaced manual entry bottlenecks with automated scripts, ensuring a consistent, structured data architecture for both “Cars” and “Buses” databases.
This donut chart visualizes the distribution of EV bus data across three primary sources (CCP, Fiatla2ee, and MotoWheeler) successfully integrated into the internal database via automated scraping pipelines.
  • Robustness & Scalability: By implementing Python-based scraping (BeautifulSoup/Selenium), I ensured the system could autonomously handle disparate units of measurement and currency conversions, significantly reducing the margin for human error.
  • Sensitivity Analysis Framework: I developed a modular analysis script to evaluate how fluctuations in fuel costs, vehicle acquisition prices, and operational expenditures impact the “break-even” point for EV adoption.

Automation & Data Infrastructure

The technical backbone of my contribution was the development of a comprehensive Python-based automation suite.

  • Web Scraping Engine: Utilized Selenium and BeautifulSoup to navigate complex web structures, extracting real-time data on vehicle specifications.
  • Data Normalization: Implemented automated scripts to harmonize diverse units of measurement (e.g., converting various energy metrics into a standardized format).
  • Sensitivity Analysis Scripting: Developed a Python module for multi-variable analysis. By iterating through variables such as EV_Price_Percentage and km_per_annum, the system generates heatmaps (hexbin plots) to identify optimal investment windows for stakeholders.
This hexbin plot visualizes the TCO of ICE vehicles, mapping fuel costs against consumption rates to highlight favorable economic transition zones. 🔍 Click to expand
This hexbin plot visualizes the TCO of ICE vehicles, mapping fuel costs against consumption rates to highlight favorable economic transition zones.
This hexbin plot visualizes the TCO of EV vehicles, mapping price percentages against annual mileage to highlight economic break-even points. 🔍 Click to expand
This hexbin plot visualizes the TCO of EV vehicles, mapping price percentages against annual mileage to highlight economic break-even points.
Enlarged visualization

Quantified Impact & Operational Savings

  • Lead Time Reduction: Automated data collection reduced the time required for market research by approximately 70%, enabling the consulting team to pivot toward high-value strategy development.
  • Data Integrity: Standardized the TCO calculation logic, eliminating manual errors and ensuring consistent reporting across multi-client projects (e.g., UNDP, World Bank).
  • Decision Support: The sensitivity analysis tools provided clients with a robust framework to model various economic scenarios, directly influencing policy recommendations for regional energy regulators.