NICHOLAS BORG

Passionate about AI, fascinated by data, and driven to simplify complexity.

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Who I Am

A professional headshot of Nicholas Borg

Hey, I'm Nick - a data analyst who enjoys working with generative AI to simplify everyday tasks. With a foundation and interest in human cognition, I always try to approach technology thoughtfully, aiming to build tools that feel intuitive and genuinely helpful for users.

On a typical day, you'll find me raving about the newest AI models, building new tools, or fine-tuning data pipelines. When I'm not working with data, I'm probably experimenting with coffee brewing techniques, dabbling in astrophotography, or exploring Azeroth in World of Warcraft.

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Selected Work

AI Job Matchmaker Agent visualisation

AI Job Matchmaker Agent

An autonomous AI agent that reads a CV, finds relevant job openings on live job boards, and writes personalised cover letters for the best matches.

The Problem

The modern job search is a high-volume, repetitive process. Manually tailoring a CV, searching multiple job boards, and writing unique cover letters for each application is time-consuming. My goal was to explore Google's Agent Development Kit (ADK) to see if this complex workflow could be automated by a chain of specialised AI agents.

My Approach & Tools

I designed and built a multi-agent system to automate the entire process from CV to cover letter. The system operates as a pipeline, with specialised agents handing off tasks to one another. The core of this project was built using Python and Google's Agent Development Kit (ADK), leveraging the Gemini family of models for language understanding and generation.

The Result

The agent successfully processed my CV and identified three highly relevant roles: a Data Analyst at Newton, a Data Analyst at Foodsmart, and a Lead Data Engineer at Open Architects. For each role, it generated a well-structured, context-aware cover letter ready for submission. The entire process, from reading the PDF to generating the final letters, was completed in under a minute.

Open Notebook

Community Analysis of /r/Malta Subreddit

A comprehensive data analysis exploring sentiment, engagement patterns, and discussion themes within Malta's largest online community.

The Problem

Online communities generate vast amounts of unstructured conversation data, but extracting meaningful insights from thousands of posts and comments is challenging. Understanding what drives engagement, what topics resonate, and how sentiment shifts over time requires systematic analysis.

My Approach & Tools

Using Python and the Reddit API (PRAW), I collected historical post data and performed sentiment analysis, topic modelling, and engagement metrics analysis. I used natural language processing libraries and data visualisation tools to identify trending topics, peak activity times, and community sentiment patterns.

The Result

The analysis revealed key insights about the community's interests, including dominant discussion themes around local politics, tourism, and daily life. Sentiment analysis showed generally positive community interactions, while engagement patterns highlighted optimal posting times and content types that drive conversation.

Open Notebook

Marvel Cinematic Universe Data Analysis

An analytical deep-dive into the MCU's box office performance, audience reception, and narrative patterns across 30+ films spanning over a decade.

The Problem

The MCU represents one of cinema's most successful franchises, but what factors truly drive its success? With varying box office returns, critical reception, and audience scores, understanding the correlation between film characteristics and performance metrics requires data-driven analysis.

My Approach & Tools

I aggregated data from multiple sources including box office databases, review aggregators, and the MCU wiki. Using Python, pandas, and visualisation libraries, I analysed correlations between budget, runtime, release timing, character appearances, and financial/critical success to identify success patterns.

The Result

The analysis uncovered interesting trends: ensemble films significantly outperform solo outings, May releases historically deliver stronger returns, and there's a surprisingly weak correlation between critical scores and box office performance. Phase-by-phase analysis also revealed evolving audience preferences over time.

Open Notebook

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Whether it's about data, AI, an opportunity, or just an interesting idea - I'm always open to conversation and collaboration.

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