CV
Education
| Master of Business Analytics | 2024 – 2025 |
| Grade (CGPA): 3.83 / 4.00 | |
| Monash University – Melbourne, Australia |
| Bachelor of Business and Commerce | 2020 – 2023 |
| Major: Business Analytics | |
| Monash University – Kuala Lumpur, Malaysia |
Professional Accreditations
- Statistical Society of Australia (SSA) - Graduate Statistician (GStat)
Skills
| Software |
| RStudio, Git, Tableau, Power BI, Oracle Data Modeler, MongoDB, SAS EM, MySQL |
| Languages |
| R, HTML, css, SQL, DAX, NoSQL, Python |
| Cloud |
| AWS, Google Cloud |
Projects
| Australian Migration & Labour Market Dashboard (RShiny) |
- Developed a multi-tab interactive RShiny dashboard integrating multiple ABS datasets to analyse Australian labour force dynamics across migration, employment, and education dimensions.
- Performed extensive data cleaning, transformation, and alignment in R to support cross-variable analysis, building interactive visualizations including geospatial maps, Sankey flows, time-series comparisons, and distribution plots.
- Applied statistical techniques such as linear regression within the dashboard to explore relationships (e.g. education and income), presenting results directly for user interpretation.
| Applied Retail Forecasting & Model Evaluation (ABS Data) |
- Conducted an end-to-end forecasting analysis on Australian retail turnover data, examining long-term trends, seasonality, and structural disruption around COVID-19 using variance stabilization, decomposition, and stationarity diagnostics.
- Developed and compared ETS and ARIMA models based on data characteristics, theoretical suitability, and information criteria, evaluating trade-offs between forecast stability and responsiveness using rolling test sets and multiple accuracy metrics.
- Validated forecasts against newly released ABS data beyond the original sample period and outlined a repeatable operational workflow for annual model review, refitting, and performance monitoring.
| Service Flow Simulation Tool |
- Developing a simulation tool to explore service-flow dynamics, modelling customer arrivals, service times, and queue selection behaviour using probabilistic distributions to reflect real-world uncertainty.
- Implemented logic where customers dynamically join the shortest available queue, enabling analysis of congestion, waiting-time behaviour, and system performance under varying arrival and service conditions.
- Designed the tool to support scenario testing across different parameter settings; the current version demonstrates core simulation logic, with additional distributions and controls in progress.
| YouTube Analytics ETL Pipeline & Power BI Dashboard (Ongoing) |
- Building an end-to-end ETL pipeline extracting YouTube channel and video metadata via the YouTube Data API, persisting time-stamped raw snapshots to support reproducibility and trend analysis.
- Implementing transformation and data modelling logic in DuckDB using SQL to produce analytics-ready dimension and fact tables, alongside derived KPIs such as subscriber growth, view growth, and upload cadence.
- Designing a refreshable Power BI dashboard to visualise channel growth, content performance, and portfolio-level insights using curated datasets.
Awards
SEEK Educational Support Grant - Awarded competitive educational funding by SEEK in recognition of academic performance and contribution.
Monash Faculty of IT, Award of Excellence - Honoured for exceptional project in FIT5057 – Project Management, selected from over 250 groups.
Experience
| Business & Data Analyst Intern | |
| SEEK – Cremorne, 3121, VIC, Australia | August 2025 – November 2025 |
- Integrated SEEK internal datasets with external sources including ABS, Census, and JSA using SQL and AWS Athena, assessing data quality and coverage across geography and occupation at multiple levels (national, state, SA4/SA3, and 1–4 digit ANZSCO classifications).
- Designed and maintained automated data pipelines in R to pull, clean, and reconcile large-scale datasets, producing refreshed dashboards and analytical reports through a fully reproducible, push-button workflow.
- Applied statistical methods including correlation analysis, RMSE-based model evaluation, forecasting techniques, and Henderson moving averages to de-seasonalize labour-market indicators and assess trend reliability.
- Optimized data processing speed and efficiency to support frequent refresh cycles, enabling timely insights for internal sales and product teams with specific commercial and operational requirements.
- Contributed to the development of a scalable labour-market analytics product with projected $10–12M revenue potential, supporting data monetization and strategic decision-making.