*This a generalised one page version of my resume and not indicative of every experience. For particular roles, please reach out and I can share relevant experiences.

PDF Version available here

Skills Summary

Overview

Slyp

2020-2024

PwC

Senior Associate (2019 - 2020)/ Associate (2018 - 2019)/ Trainee (2017 - 2018)

Other Interests

Data Skills: Model Development & Deployment | A/B Testing & Experimentation | Customer Segmentation & Targeting | Visualization (Tableau, PowerBI, Looker, QuickSight) | Attribution Modeling | Causal Inference Techniques | Data Modeling

Technologies: Python | SQL | Cloud Platforms (AWS, GCP) | Distributed Computing | Data Pipeline Development | GIT and Code Control

Domain Knowledge: FinTech and Banking | Retail | Marketing Analytics | Insurance | News & Media

I am an experienced Data Scientist/Analyst with 6 years working across consulting and start up. I began my career at PwC in the Cloud and Data practice. I then transitioned to Slyp, a fast growing financial technology scale-up.

I was one of Slyp’s first hires in the data team and saw it through significant growth in this period. My role encompassed a hybrid function involving Data Architecture, Data Modelling, Analytics, Data Science and Machine Learning. Through this, I have a broad skill set and deep experience building data products both internally and externally.

Education: Master of Statistics and Data Science at UNSW (2019 - 2022) | Diploma of Business UNSW (2017-2018)

Slyp is a payments FinTech started by former PayPal employees that links bank transactions (40% of Australian Payments) with merchant receipts to deliver digital receipts and insights for integration partners (banks, merchants, schemes). As one of the first hires in Slyp’s data team, I contributed to its significant growth. Key Achievements include:

  • Developed data strategy with C-Suite. I managed external consulting projects and data partnerships across 5+ projects.

  • Designed and launched Slyp's commercial data product for retailers, analyzing data from 2000+ retailers to provide insights such as industry indexes and benchmarks, share of wallet and customer leakage modeling, demographic and customer clustering, price elasticity, and SKU/product analysis (attachment and affinity rates).

  • Spearheaded data migration from PostgreSQL RDS to a Data Lake architecture, establishing data foundations for Redshift, QuickSight, and SageMaker, reducing analytics request time by over 80%.

  • Automated internal business department reports and product analytics reports using AWS QuickSight (visualization), Redshift (Data Modeling), and Glue (ETLs), enhancing efficiency and accuracy, reducing time spent on reporting by 90%.

  • Analyzed digital payment fields with SQL, optimizing partitioning strategy in Slyp's NoSQL database, cutting transaction/receipt matching time by 70% with minimal risk of false positives.

  • Partnered with Finance, Marketing, Sales, and Product teams to run experimentation (A/B testing) to discern the effectiveness of changes in product or campaigns and allocate spending.

See more about Slyp on their website

See my projects with Slyp in my Portfolio

As a technology consultant at PwC, I honed my technical skills in ETL development, data modelling, data science, machine learning, and data visualization while managing senior stakeholders and projects. Key project highlights include:

  • Media Company: Led data modelling and developed newsroom dashboards in LookerStudio and BigQuery, optimizing performance and reducing costs. The platform surfaced near real-time data to 1000+ journalists as a web and phone app which improved data-driven decisions and led to a +133% YoY increase in new subscriptions.

  • Health Insurer: Designed a PySpark ETL framework on AWS, streamlining data capture and boosting analyst efficiency. Contributed to claims leakage modeling in Python, resulting in an 8% increase in identified claim fraud.

  • State Government: Coordinated workshops with department officials to define data model and dashboard requirements, then led implementation in BigQuery using SQL, as well as the build of dashboards. The automated reporting was used by department employees, ministers of parliament and NGO partners.

  • State Government: Implemented a data matching algorithm utilizing advanced analytical capabilities (e.g. metaphone 3), enabling seamless identification of citizens with multiple cases across departments so that their cases could be better cross-managed.

  • Bank: Developed Python scripts to identify non-compliant home loan files locally, enabling creation of clean training data for machine learning. This initiative rectified over 11% of existing files to meet industry reporting standards, leading to increased revenue via re-classification of loans.

See my projects with PwC in my Portfolio

Volunteering: During my tenure at PwC, I utilized volunteering hours to collaborate with Orange Sky Laundry, supporting people experiencing homelessness. I analyzed volunteer data to design a customer journey and insights report for onboarding on their volunteering technology platform.

French: I speak intermediate French (CEFR Level B1) and undertook a working sabbatical in France as a Chalet Host/Cook.

WSET: I hold my level I and II certification from the wine and spirits education trust. I spent part of my recent sabbatical participating at a wine producer.