While working on a predictive churn model, the product team needed to understand the concept of logistic regression to interpret the results.

My task was to convey how logistic regression predicts probabilities and what the coefficients mean in plain language.

I created a simple analogy comparing odds to a coin flip, used visual aids showing probability curves, and avoided jargon by focusing on outcomes rather than formulas.

The team grasped the key insights, asked informed follow‑up questions, and incorporated the model’s recommendations into the product roadmap, leading to a 10% reduction in churn over the next quarter.

During a clinical trial analysis, I noticed unusually high variance in the primary endpoint across sites.

Identify the source of variance and ensure the dataset was reliable for regulatory submission.

I wrote R scripts to run completeness checks, visualized missing patterns with heatmaps, discovered a systematic coding error in one site’s data entry, and coordinated with the data manager to correct the records.

After cleaning, the variance stabilized, the analysis met protocol specifications, and the study received FDA approval without delay.

The marketing department needed a churn prediction model within 48 hours to inform a last‑minute campaign launch.

Develop, validate, and present a reliable model under the time constraint.

I prioritized rapid data extraction using SQL, employed automated feature engineering with the 'recipes' package in R, used cross‑validation to quickly assess performance, and prepared a concise slide deck focusing on key metrics and actionable recommendations.

The model achieved an AUC of 0.82, the campaign was launched on schedule, and it generated a 7% lift in conversion compared to the previous baseline.