story
I'm a data scientist who cares about getting the analysis right and explaining it clearly.
My path to data science
[PLACEHOLDER: A short, honest narrative — your prior background, what drew you to data, and what you have learned so far. Two or three short paragraphs maximum.]
[PLACEHOLDER: What kind of team you want to join and what you are building toward. A genuine early-career story reads better than an inflated one.]
Education
[PLACEHOLDER: Degree or program]
[PLACEHOLDER: Institution] · [PLACEHOLDER: years]
[PLACEHOLDER: relevant coursework — only what was actually completed or is in progress, labeled accordingly.]
Experience
Transferable experience counts — real roles belong here even if they weren’t data roles, with a line on what they taught. If there’s no formal experience yet, this becomes a learning timeline of courses and project milestones.
[PLACEHOLDER: Role or milestone]
[PLACEHOLDER: Company / school / program] · [PLACEHOLDER: dates]
[PLACEHOLDER: one or two sentences — what you did and what it taught you that transfers to data work.]
[PLACEHOLDER: Earlier role, course, or certification]
[PLACEHOLDER] · [PLACEHOLDER: dates]
[PLACEHOLDER: what was learned or accomplished.]
Skills at a glance
| Area | Core tools |
|---|---|
| Languages | Python, SQL |
| Analysis | pandas, NumPy, Jupyter |
| Machine Learning | scikit-learn |
| Statistics | Hypothesis testing, regression |
| Visualization | Matplotlib, Seaborn, Plotly |
| Workflow | Git, GitHub, virtual environments |
How I work
Start with the question, not the model
The best method is the simplest one that genuinely answers the question.
Baselines before complexity
A model is only impressive relative to what a simple approach achieves.
Show the caveats
An analysis that hides its limitations isn't analysis — it's advertising.
Reproducibility is respect
Work someone else can rerun is work someone else can trust.
Want the short version?
The résumé covers the same ground in one page — or reach out directly and I’ll respond quickly.