💼 Open to: Junior Data Analyst · Reporting Analyst · Insights Analyst · People/HR Analytics — Melbourne, Australia
🌐 Full portfolio, project writeups & blog: ahmed-al-rafsan.github.io
I turn messy, raw data into decision-ready insights — and I present them the way stakeholders actually consume them. I don't just build dashboards; I find the business story behind the numbers and turn it into recommendations people can act on.
My path is a deliberate stack: Aeronautical Engineering (systems thinking) → MBA in HRM (people & business logic) → Master of Business Information Systems, Data Analytics (the technical core). Before moving into analytics full-time, I owned the enterprise performance-reporting cycle for 500+ employees at Kazi Farms Group, reporting directly to CEO and GM level — so I learned to speak stakeholder before I learned DAX.
Most recently: a live six-month industry capstone as Team Lead & Data Analyst for a real Australian client — a national school-targeting ML pipeline, a Top-200 outreach list delivered into the client's CRM, and an executive Power BI dashboard, handed over end-to-end.
class DataAnalyst:
def __init__(self):
self.name = "Ahmed Al Rafsan"
self.based_in = "Melbourne, Australia 🇦🇺"
self.role = "Data Analyst — Workforce & Business Analytics"
self.education = ["MBIS (Data Analytics) — completing 2026",
"MBA (Human Resource Management)",
"BEng Aeronautical Engineering"]
self.daily_stack = ["SQL", "Power BI + DAX", "Python (pandas, scikit-learn)",
"Excel + Power Query", "Tableau"]
self.edge = "3 domains × 3 countries — engineering rigour + HR stakeholder logic + data"
self.now_pursuing = ["Microsoft PL-300", "Azure DP-900"]
self.portfolio = "https://ahmed-al-rafsan.github.io"
def mission(self):
return "Turn messy data into decisions people can defend in a boardroom."Every project follows the same logic — it leads with the business question, never the tool:
flowchart LR
A(["🎯 Business<br/>Problem"]) --> B["🧹 Clean & Validate<br/>PCUVCOD"]
B --> C["🔍 Analyse & Query<br/>SQL · Python"]
C --> D["📊 Model & Visualise<br/>Power BI · Tableau"]
D --> E["💡 Insights &<br/>Recommendations"]
E --> F(["⚠️ Limitations &<br/>Next Steps"])
PCUVCOD — my data-cleaning framework: Profile → Completeness → Uniqueness → Validity → Consistency → Outliers → Document. Seven checks, same order, every dataset, no exceptions.
| Languages & Querying |    |
| BI & Visualisation |      |
| Analysis & ML |     |
| Workflow & AI |      |
| Exploring Next |    |
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Team Lead & Data Analyst on a six-month engagement for a real Australian small-business client. Led a 6-person cross-functional team end-to-end — from raw government data through to live client handover. Architected the full Python ML pipeline on a 9,855-school national dataset: cleaning, feature engineering, a custom Priority Score, K-Means segmentation (k=4), and a Random Forest classifier (AUC 0.972) on the held-out test set. Deliberately excluded the target-correlated feature from model inputs to prevent data leakage — a design choice that made the priority logic independently defensible at handover. Delivered 3,344 high-priority schools identified, an operational Top-200 outreach list ingested directly into the client's CRM, and a 2-page executive Power BI dashboard.
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Paired hands-on HR domain experience with machine learning to predict turnover across 1,470 employee records. Logistic Regression + Random Forest with balanced class weights, 11 SQL business queries, and a 2-page Power BI dashboard with 8 custom DAX measures scoring every employee into a High / Medium / Low risk tier — 344 employees flagged for HR action.
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Analysed 1M+ UK online-retail transactions through a 7-script pipeline (1.07M rows cleaned to 805K) and an RFM scoring system classifying 5,878 customers into 7 behavioural segments — surfacing a 177× Customer Lifetime Value gap. Headline finding: 75–80% of new customers churn within the first month — onboarding is the single highest-leverage retention fix.
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Root-cause investigation of an 89% revenue decline across 24 months of sales data. 10 SQL queries (GROUP BY, HAVING, LAG) traced the collapse to 92% of customers going inactive — with the top customer, worth 12.7% of total revenue, going dormant. Delivered 4 executive recovery recommendations via a 4-page dashboard.
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Explored 20+ years of Victorian Government open data — identified 45% growth in CBD business establishments and flagged vacant-space trends as a forward operational-risk indicator for the city's recovery story. |
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Analysed 260k+ chip transactions, segmented customers by lifestage × premium tier, evaluated trial-store layout uplift across 3 locations. |
Conditional aggregation on 50k+ records, PII anonymisation aligned with the Australian Privacy Act, and a 5-table relational schema designed in 3NF. |
Tableau dashboard for factory-telemetry downtime analysis (160k+ IoT records across 4 factories) and a forensic pay-equity classification for an HR audit. |
- 🎓 Google Advanced Data Analytics Professional Certificate — Coursera, 2026
- 🎓 IBM Data Analyst Professional Certificate — Coursera, 2026
- 📊 Creative Designing in Power BI — Microsoft / Coursera, 2026
- 🏢 3 × Forage industry simulations — Quantium · Commonwealth Bank · Deloitte Australia, 2026
- 📊 Microsoft PL-300 — Power BI Data Analyst — in progress
- ☁️ Microsoft DP-900 — Azure Data Fundamentals — in progress
Education: Master of Business Information Systems (Data Analytics) — AIH Melbourne, completing 2026 · MBA (HRM) — North South University, 2019–2021 · BEng Aeronautical Engineering — Nanchang Hangkong University, 2014–2018
flowchart LR
A["✅ NOW<br/><b>Data Analyst</b><br/>SQL · Power BI · Python"] --> B["🔜 2027–28<br/><b>Analytics Engineer</b><br/>dbt · Git · Cloud Warehousing"]
B --> C["🎯 2028+<br/><b>AI Analytics Engineer</b><br/>ML in production"]
Every major project ships with a stakeholder-style walkthrough — findings presented the way an analyst presents to a business meeting, not a code tutorial.
"Data analysis isn't about making charts. It's about finding the story behind the numbers and turning it into business decisions."
📍 Melbourne, Australia · 🎓 MBIS (Data Analytics), AIH · 🌐 ahmed-al-rafsan.github.io · 📧 ahmed.rafsan108@gmail.com