Joseph
Saba

Analyst & Builder

I'm a data analyst who also builds full-stack analytics platforms

My background is in FMCG and retail, working across scan data, consumer panels, and market research to drive commercial decisions. That work has increasingly led me to build dashboards, reporting pipelines, and full-stack analytics tools in Python, SQL, and lightweight web frameworks.

Years Experience
10+
across analytics, reporting, and commercial decision support
Projects Delivered
20+
dashboards, models, pipelines
Location
Melbourne, AU
available in-person, remotely, and for coffee
Python Streamlit BI
Category Dashboard
Built to replicate the category reporting workflows I used across Nielsen, Bakers Delight, and BIC - with live filtering by retailer, category, segment, and brand.
Python Plotly Research
Lego Range Review
Exploratory analysis of Lego’s product range from the 1980s to 2025, combining pricing, product mix, and market context to understand the drivers behind its long-term growth. Built as a structured research project using Python and Plotly, then presented as a visual analysis deck.
JavaScript Public Data Data Visualisation
LGA Language Explorer
What languages are spoken in your suburb - and how has that changed over 15 years? Interactive explorer built on harmonised ABS Census data across 8,465 suburbs and 549 LGAs, with projections to 2026. Search any area in Australia.
Python SQLite FastAPI HTMX
Trading Performance
Full-stack trading analytics platform built in Python, FastAPI, HTMX, and SQLite to track performance, discipline, and edge quality over time. Includes behavioural scoring, setup analysis, weekly scorecards, and projection tooling designed to connect trading process to outcomes.
Languages
Python SQL JavaScript HTML / CSS
Frameworks
FastAPI HTMX Streamlit Plotly
BI & Data
Power BI SQLite PostgreSQL Pandas / NumPy
What I Build
Dashboards Reporting Automation Data Pipelines Web Apps
2025 – Present
Current
Data Analyst & Developer
Self-Employed
Building analytics applications in Python - from data ingestion and transformation through to dashboards, reporting workflows, and full-stack tools. Current projects include a performance analytics platform with behavioural scoring, setup drift detection, and Monte Carlo projections, built end-to-end in FastAPI, SQLite, and HTMX.
2021 – 2025
Category Executive
BIC
Led commercial and performance analysis across sales, consumer, and market datasets, translating complex data into recommendations for marketing, sales, and leadership teams. Built self-serve dashboards in Excel and Power BI, and developed the business case that helped reverse a key product range from -48% decline to +18% growth.
2018 – 2021
Category Insights Analyst
Bakers Delight
Helped build the reporting infrastructure behind a 700+ location network, working with data engineers on databases, Sisense dashboards, and automated reporting. Developed analytical models across pricing, promotions, and performance data, and turned multi-source data into practical recommendations for operational and regional teams.
2015 – 2018
Client Service Executive → Senior Client Service Executive
Nielsen
Built my analytical foundation working across large retail and consumer datasets, helping major clients interpret performance, data methodology, and market trends. Delivered recurring and ad hoc analysis, translating complex data into clear recommendations for non-technical stakeholders.
2011 – 2015
Bachelor of Commerce (Marketing, Statistics)
Bachelor of Information Systems
Deakin University
Python SQLite FastAPI HTMX
Trading Performance
Overview
Full-stack analytics platform built to measure and improve trading performance over time. Tracks P&L, win rates, setup effectiveness, and behavioural patterns through discipline scoring, mental state correlation, setup drift detection, and Monte Carlo projections. Built from scratch in Python using FastAPI, HTMX, and SQLite to answer one question — is the edge real, and am I following it?
Screenshots
Performance Dashboard Edge Trend Weekly Scorecard Projection Module
Python Streamlit BI
Category Dashboard
Overview
Interactive analytics dashboard built in Python and Streamlit to track growth, share, and brand performance across multiple dimensions. Inspired by real reporting workflows and rebuilt as a more flexible decision tool, with live filtering by retailer, category, segment, and brand.
Screenshots
Category Dashboard
Python Plotly Research
Lego Range Review
Overview
Exploratory analysis of Lego’s product range from the 1980s to 2025, combining pricing, product mix, and market context to understand the drivers behind its long-term growth. The analysis found that Lego’s expansion was driven not by a single factor, but by a mix of licensed IP, the adult market, and a premiumisation strategy that significantly increased average price per piece.
Screenshots
Lego - Slide 3 Lego - Slide 4 Lego - Slide 5 Lego - Slide 6 Lego - Slide 7 Lego - Slide 8 Lego - Slide 9 Lego - Slide 10 Lego - Slide 11
JavaScript Public Data Data Visualisation
LGA Language Explorer
Overview
Australian census geography changes between editions - suburbs split, LGAs merge, boundaries shift - which makes direct trend comparison unreliable. I rebuilt the data from SA1 level using ABS correspondence files to harmonise 2011, 2016, and 2021 onto consistent 2021 boundaries, then added a 2026 projection layer. The result covers 8,465 suburbs across 540 LGAs with consistent time-series data on languages spoken at home. The full methodology is written up in the blog post.
Screenshots
Language Explorer