Nicholas Borg
§ 01 Profile

I think things up.
Then I build them.

I'm Nick. I'm a data engineer with a cognitive science background, and I have a habit of turning curiosity into something that actually runs. Half my day is spent picking ideas apart. The other half is spent building them.

See selected work Read recent writing
§ 02 About a brief introduction
Portrait of Nicholas Borg Nick   data engineer

I build AI-driven tools: pipelines that run in the background, and agents that do work I wouldn't get through 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 signal - are the ones I'm still working with in code. Engineering 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
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

Four agents pass work between each other - one parses the CV, one pulls jobs, one ranks them, one drafts. Python on Google's Agent Development Kit, with Gemini handling the language work.

Result

Fed it my CV and got three plausible matches back with cover letters attached: Data Analyst at Newton, Data Analyst at Foodsmart, Lead Data Engineer at Open Architects. End-to-end runtime 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 enormously successful, but the why is messy. Does budget predict box office? Release timing? Ensemble cast? Critical scores?

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 knot of half-formed ideas and get slowly untangled into something readable. AI helps me chase sources and pressure-test arguments. It doesn't write the thing for me, and I wouldn't want it to.

fetching latest articles…

All articles on Medium
§ 05 Contact say hello

Got an idea worth poking at?

Email [email protected]
Elsewhere
Open to Data engineering and AI tooling. Open to weird side projects.