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Analytics & Data
Turning raw data into patterns, insights and decisions through statistical analysis, machine learning and structured data workflows.
I see skills as a means to solve problems, not as a checklist of software I know how to use.
My academic background combines Finance, Business Intelligence & Analytics, and Data Science. That means I am comfortable moving between a business question, a dataset and a financial decision — and translating between them.
Turning raw data into patterns, insights and decisions through statistical analysis, machine learning and structured data workflows.
Building analytical views that make information easier to understand, compare and act upon.
Applying financial thinking and quantitative methods to understand businesses, markets, investments and risk.
Comfortable working with data from collection and transformation through analysis and presentation.
The technical side only matters when it helps solve the underlying business problem. I enjoy structuring ambiguous questions and turning them into actionable analysis.
The work I enjoy most sits at the intersection of technical analysis and practical decision-making. A typical problem might move through several stages.
Start with the business question rather than immediately jumping into the data.
Break an ambiguous problem into measurable questions, variables and assumptions.
Use Python, SQL, statistical methods, financial models or BI tools depending on what the problem actually requires.
Turn the analysis into a clear story that stakeholders can understand and use.
Focus on the implication of the analysis: what should happen next?
I am continuing to build depth across analytics, finance and business — while learning how to connect the three more effectively.