CRM Workflow Automation & Customer Insights
Data Analyst / Project Lead – Truetel, Melbourne | Apr 2024 – Mar 2025
Software & Tools: Python, SQL, Excel, Zoho CRM, Google Sheets
→ Developed automated lead management and customer engagement workflows using Zoho CRM.
→ Implemented data pipelines to migrate and clean customer data, improving CRM usability and campaign tracking.
→ Applied rule-based customer segmentation for improved targeting and engagement.
Strategic Relevance: Showcases customer segmentation, ETL workflows, and cross-team collaboration.
Forecast Optimization for Parcel Logistics
Data Science Intern – Australia Post, Melbourne | Apr 2024 – Dec 2024
Software & Tools: Python, Dataiku DSS, GCP (BigQuery), Tableau, Excel, SQL, Git
→ Enhanced parcel volume forecasting using hierarchical reconciliation; improved accuracy (MAPE reduction of 1%).
→ Built automated pipelines for demand forecasting and reporting within Dataiku DSS.
→ Conducted A/B comparisons for model evaluation and communicated results via Tableau dashboards.
Strategic Relevance: Highlights statistical analysis, A/B testing, forecasting, and data visualization.
Real-Time CNN Pipeline for Radio Astronomy
Research Project – Centre for Astrophysics and Supercomputing, Melbourne | Oct 2019 – Jan 2024
Software & Tools: Python, TensorFlow, Keras, NumPy, SciPy, Slurm, HPC
→ Developed a GPU-accelerated CNN pipeline for real-time classification of astrophysical signals.
→ Applied DBSCAN clustering to reduce RFI noise and streamline input to classifiers.
→ Conducted regression modeling to improve detection rates across varying input conditions.
Strategic Relevance: Demonstrates model building, MLOps practices, clustering, and noise filtering.
Statistical Modeling & Bayesian Inference for Pulse Fitting
Research Project – Centre for Astrophysics and Supercomputing, Melbourne | Oct 2019 – Jan 2024
Software & Tools: Python, NumPy, SciPy, Pandas, Matplotlib, Jupyter
→ Used Bayesian inference for parameter estimation and model comparison.
→ Evaluated models using AIC, chi-square, and likelihood-based criteria.
Strategic Relevance: Demonstrates understanding of statistical modeling and risk-based evaluation.
Large-scale Signal Processing for Galaxy Imaging
Masters Researcher – Max Planck Institute for Radio Astronomy, Bonn, Germany | Oct 2017 – Jan 2018
Software & Tools: Python, JURECA HPC, NumPy, Astropy, Matplotlib, Git
→ Processed 100+ TB of astronomical data using Python pipelines for galaxy imaging and magnetic field analysis.
→ Built 3D visualizations and automated scripts to analyze star-forming regions and distance estimation.
Strategic Relevance: Strong ETL, large-scale data processing, and scientific computing experience.
Data Visualization of Star-Forming Regions
Research assistant – Max Planck Institute for Radio Astronomy, Bonn, Germany | Mar 2017 – Oct 2017
Software & Tools: Python, NumPy, Astropy, Matplotlib
→ Built 3D visualizations to analyze galactic star-forming regions and identify distance-related patterns.
→ Integrated multi-source datasets to support spatial analysis and improve interpretability of key variables.
Strategic Relevance: Statistical Analysis, Strong communication and presentation skills