Projects with purpose.
These aren't just things I built — they're problems I solved. Each one has a story, measurable impact, and lessons that shaped how I approach the next challenge.
AI-Powered HR Analytics Assistant
HR data is rich, complex, and often locked behind technical barriers. Business users needed a way to ask questions about workforce data in plain language — without waiting for an analyst. At the same time, we needed to handle sensitive employee data responsibly.
in testing
to insights
exploration
The Challenge
Stakeholders across HR, finance, and leadership needed quick answers from workforce data — headcount trends, retention patterns, diversity metrics. But these requests often bottlenecked through a small analytics team. We needed a solution that was both powerful and responsible.
The Approach
I architected a solution using Databricks Genie as the conversational interface layer, backed by structured HR data models. For sensitive use cases like anonymising employee feedback, I explored locally-hosted open-source models to ensure data never leaves our environment.
Key Outcomes
Data Lake Modernisation & Cost Optimisation
Our corporate data lake was growing expensive and slow. Storage costs were ballooning, and processing times were hurting downstream reporting. It was time for a fundamental rethink of how we stored and accessed data.
Azure storage costs
improved processing
company-wide reporting
The Challenge
Data was stored in various formats across the Azure Data Lake. Some processes were brittle, redundant, and expensive to maintain. The finance team was asking questions about cloud costs, and the analytics team was waiting too long for data refreshes.
The Approach
I led the migration of core corporate datasets to Parquet format — a columnar storage format that dramatically reduces storage footprint while improving read performance. I also rebuilt fragile ETL automations using Python, replacing manual, error-prone processes with robust, testable pipelines.
Key Outcomes
Energy Renovation Decision Support Tool
In the Netherlands, social housing makes up 31% of the housing stock, and by law, 70% of tenants must consent before energy renovations can proceed. Housing associations needed to understand what tenants actually wanted — not just assume.
is social housing
required by law
website
The Challenge
Two key challenges emerged: understanding diverse tenant preferences for different renovation measures, and giving housing associations a practical tool to evaluate renovation packages based on those preferences. The goal was to accelerate the energy transition while respecting tenant voice.
The Approach
I designed a stated preference survey to collect structured data on tenant priorities. Using discrete choice models in R, I quantified how different tenant segments valued various renovation features. The insights were packaged into an interactive R Shiny web application that lets housing association decision-makers visually explore trade-offs.
Key Outcomes
Overseas Supply Chain Optimisation
Before data was my primary tool, I was already using data to solve operational problems. At MAHLE, I applied systematic analysis to a logistics challenge that was eating into margins.
reduction
efficiency
EGR line
The Challenge
MAHLE's overseas supply chain was suffering from high freight costs and inefficient transportation routes. The logistics team needed fresh eyes on route design and freight negotiations.
The Approach
I analysed supplier bases, customer locations, child part lists, volume consumption, bin sizes, distances, and transportation costs. Based on this data, I designed optimised milk routes and presented negotiation points to freight forwarders.