◈ — Lab
Playground.
Data science turned inward. A self-portrait in DataFrames, intelligence reports, and compound growth curves. Hover, click, explore.
◈ — If I were a dataset
What does a data scientist look like as data?
>>> import rin
>>> rin.info()
RinDataFrame — 1 row × 11 columns
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dtypes: bool(3), int64(3), object(5)
memory_usage: not applicable — experience doesn't compress
>>>
For reference only · self-assessed metrics calibrated against role deliverables · generated with the assistance of an AI tool (Claude · Anthropic)
◈ — Intelligence Report
Analyse Me.
Three interactive lenses on the same dataset — my career. Each one tells a different part of the same story: deliberate growth, compounding breadth, and the principle that continuous improvement is not a soft skill. It's infrastructure.
Plotted against the typical career trajectory for analytics professionals at each year of experience. Bubble size = cross-domain breadth (number of distinct technical domains actively used). Source: APS Career Pathfinder (APSC 2024) · LinkedIn Work Change Report (2024) · IAPA Skills & Salary Survey (2023).
For reference only. Career seniority scores and domain depth values are self-assessed approximations calibrated against real role deliverables. Industry benchmarks are derived from publicly available sources: IAPA Skills & Salary Survey (2023), LinkedIn Work Change Report (2024), APSC Career Pathfinder and APS Workforce Data (2021–2024), and Randstad Gen Z Workplace Blueprint (2025). Individual trajectories vary significantly — this analysis reflects a particular career path, not a universal measure of performance. Generated with the assistance of an AI tool (Claude · Anthropic).