My Background
I'm a Data Scientist & BI Analyst whose path into the field started with Economics. Studying Economics gave me more than theory, it taught me how to ask better questions, challenge assumptions, and let data lead me to honest answers. That mindset has shaped how I approach every problem I work on.
Before moving fully into data science, I worked as an Assistant HR, which turned out to be surprisingly formative. Dealing with real operational data, spotting process inefficiencies, and supporting decisions that affected real people gave me a deep respect for data accuracy and governance. It's one thing to analyse clean datasets in a textbook, it's another to work with messy, high-stakes organisational data where the numbers have real consequences.
How I Work
I work across Python, SQL, Power BI, and Excel, not just as tools, but as different lenses for the same goal: turning raw data into something useful and trustworthy. Whether I'm cleaning and validating data in Python, modelling relationships in SQL, or building dashboards in Power BI, I'm always thinking about whether the output actually helps someone make a better decision.
Meaningful Work
Some of my most meaningful work has been on the Maji Ndogo projects, where I investigated access to safe drinking water and validated agricultural datasets. What I found rewarding wasn't just the technical side; it was knowing the analysis could contribute to something that genuinely matters to communities.
→ Water Funds Accountability Dashboard
→ Climate-Smart Agriculture Analytics
What Drives Me
I'm drawn to work that sits at the intersection of data quality, business intelligence, and social impact. I want to build systems that are transparent, reproducible, and grounded enough that the people relying on them can actually trust what they're seeing.
Tools & Skills
- SQL & MySQL
- Python & Pandas
- Power BI
- Excel
- Data Cleaning
- Statistical Analysis
- Data Pipelines
- Data Storytelling
- Window Functions
- Data Governance