I’m a soon-to-graduate data science student focused on practical analytics and machine learning— with an emphasis on reproducibility, clear evaluation, and stakeholder-ready storytelling.
Replace each card with real outcomes, links, and artifacts (notebooks, dashboards, demos).
Built a classification pipeline and translated top features into retention experiments and messaging.
Backtested time-series forecasts and communicated uncertainty with confidence intervals.
Extracted themes and sentiment to highlight product issues and wins; packaged results in a dashboard.
Created a lightweight framework for hypotheses, guardrails, power considerations, and interpretation.
Short posts that show how you think: methodology, evaluation, and communicating tradeoffs.
Picking metrics that reflect business cost, calibration, and the user experience of mistakes.
Definitions, ownership, refresh cadence, and the “so what” layer that drives action.
Data validation, versioning, clear assumptions, and artifact structure for clean handoffs.
Keep this aligned with the roles you want. Be specific and credible.
Add your real email, LinkedIn, and location. This form is a demo until connected to a service.
I’m interviewing for internships and entry-level roles in analytics and data science. If you’re hiring or want to talk about a project, I’d like to connect.
Email: riley.nguyen@email.com
LinkedIn: linkedin.com/in/riley-nguyen
Location: Your City, State • Open to relocation