Nicholas Borg
§ 01 Profile

I think things up.
Then I build them.

I'm Nick, a data engineer with a cognitive science background and a stubborn habit of turning curiosity into something working. Half my day is spent picking ideas apart; the other half is spent building them into pipelines, AI agents, and tools I couldn't have built on my own.

See selected work Read recent writing
§ 02

About

a brief introduction
Portrait of Nicholas Borg Nick   data engineer

I build data systems and AI-driven tools: pipelines that move quietly in the background, and agents that do the kind of work I could only daydream about doing alone.

My background is in cognitive science, and I haven't really left it. The questions that pulled me in then (how attention works, how meaning gets built out of noisy signal, how good interfaces think with you instead of at you) are the same ones I'm still chasing in code. Engineering just gave me a more honest way to test the answers.

I use generative AI the way you'd use a research assistant: to chase sources, pressure-test ideas, and tighten the writing on the way out. It speeds me up; it doesn't change what I'm trying to say.

Most days I'm untangling a pipeline or arguing with a new model. Most evenings: espresso, astrophotography, or running keys in Azeroth.

Background B.Sc. Cognitive Science
Based in Malta
Focus Data engineering · AI agents
Off-hours Coffee · Astrophotography · WoW
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§ 03

Selected Work

three projects
01

AI Job Matchmaker Agent

A multi-agent system that reads a CV, scrapes live job boards, and drafts a tailored cover letter for each shortlisted role.

Python Google ADK Gemini Multi-agent

Problem

Job hunting boils down to the same three tasks repeated: read a posting, decide if it fits, write a letter. I wanted to see how much of that a chain of agents could absorb.

Approach

I designed a pipeline of specialised agents that hand off to one another: parsing, retrieval, matching, and drafting. Built in Python on Google's Agent Development Kit, with the Gemini family handling the language work.

Result

The agent surfaced three highly relevant roles from my CV (Data Analyst at Newton, Data Analyst at Foodsmart, and Lead Data Engineer at Open Architects), with tailored cover letters. PDF in, letters out, in under a minute.

Open notebook↗
02

Community Analysis of /r/Malta

A look at how /r/Malta actually talks: sentiment, what gets traction, what people argue about.

Python PRAW NLP Topic modelling

Problem

Reddit threads are loud and unstructured. I wanted to know what drives engagement on a country-specific subreddit, and whether sentiment moves in any predictable way.

Approach

Pulled historical posts via PRAW, then ran sentiment scoring and topic modelling on the corpus and visualised it.

Result

Three categories did most of the talking: local politics, tourism, and day-to-day grumbles. Sentiment skewed positive overall - more than I'd expected. Engagement clustered tightly around evening posting windows and a handful of recurring post formats.

Open notebook↗
03

Marvel Cinematic Universe: A Decade in Data

A breakdown of MCU box office, reviews, and casting patterns across 30+ films and ten years of releases.

Python pandas Visualisation

Problem

The MCU is one of the most successful franchises in cinema, but the "why" is messy. Which signals (budget, timing, ensembles, scores) actually correlate with success?

Approach

Pulled data from box office records, review aggregators, and the MCU wiki, then ran correlations in pandas.

Result

Ensemble films beat solo outings. May releases over-perform. Critical scores barely correlate with box office. And audience taste has shifted between phases - Phase Four lands very differently from Phase One in both reviews and revenue.

Open notebook↗
§ 04

Writing

latest from Medium

I write a lot. It's how I figure out what I actually think. Articles tend to start as a tangle of half-formed ideas, then get slowly straightened into something readable. Generative AI helps me chase down sources and stress-test the sharper edges, but the argument, the phrasing, the call on what's worth saying: that's still mine.

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