In a previous role, I was tasked with analyzing customer churn for a subscription-based service. I applied logistic regression to identify key predictors of churn, such as usage frequency and customer support interactions. The analysis revealed that customers who contacted support more than twice within a month were 30% more likely to churn. Based on these insights, we implemented targeted retention strategies, reducing churn by 15% within six months.

I prioritize methodological rigor by validating models through cross-validation techniques, ensuring data quality through rigorous cleaning processes, and testing for assumptions like normality and homoscedasticity. I also collaborate with domain experts to interpret results contextually, ensuring that findings are both statistically sound and practically meaningful.

I am proficient in R, Python, and SAS for statistical modeling and data manipulation. I also use tools like SQL for database querying, Tableau for data visualization, and Git for version control. My toolkit allows me to handle diverse datasets and deliver comprehensive analyses efficiently.

I focus on simplifying complex concepts by using visual aids like charts and graphs, framing results in business terms, and emphasizing actionable insights. For example, instead of discussing p-values, I highlight how a finding can improve decision-making or drive business outcomes.

In a project for a retail client, I analyzed sales data to identify underperforming product categories. By applying cluster analysis, I segmented products into groups based on sales trends and profitability. The insights led to a targeted inventory management strategy, increasing overall profitability by 20% within a year.