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Neural Network Recommendation Engine

FEATURED

A two-tower recommendation engine deployed to 1.6 million users on the VT Markets app, driving cross-selling and product adoption among traders through personalised instrument suggestions.

PythonPyTorchPostgreSQLBigQueryBraze

Overview

Built during a data analyst internship at VT Markets, this recommendation engine personalises trading instrument content for 1.6 million users across the platform.

What it does

  • Two-tower neural network architecture matching user behaviour to relevant trading instruments
  • PostgreSQL and BigQuery ETL pipelines syncing user signals into Braze for downstream delivery
  • Automated review outreach scaled across the full user base
  • Internal LLM tooling enabling non-technical teams to run natural language queries on live data

The lesson

Recommendation systems in fintech have a cold-start problem that’s harder than most — new users have no trading history, and wrong recommendations erode trust fast. The ETL design mattered as much as the model.