Data science
Projects and writing that show the reasoning behind analysis — not just the final chart or model.
A reusable framework for connecting business questions to events, KPIs, governance, and reporting.
Problem: Teams often collect large amounts of behavioral data without a clear decision model.
Approach: Map business objectives to user journeys, measurement requirements, event definitions, and decision owners.
Results: [ADD YOUR VERIFIED RESULT]
A personal data science project exploring pace, distance, consistency, and training patterns.
Problem: Raw training logs are difficult to interpret without context and longitudinal analysis.
Approach: Clean activity data, engineer training features, visualize trends, and evaluate patterns over time.
Results: [ADD YOUR VERIFIED RESULT]
A practical scorecard for evaluating measurement maturity across implementation, governance, and usage.
Problem: Analytics quality is often discussed qualitatively and is difficult to compare over time.
Approach: Define observable dimensions and score them consistently across teams and properties.
Results: [ADD YOUR VERIFIED RESULT]
A repeatable workflow for understanding a dataset before jumping into modelling.