I led a project to predict customer churn for a telecom company. I started by defining the problem and gathering data from various sources. I then conducted exploratory data analysis to identify key features and missing values. Using machine learning models like Random Forest and Gradient Boosting, I developed a predictive model with an accuracy of 85%. I collaborated with the engineering team to deploy the model into production, setting up a pipeline for continuous monitoring and retraining. The project resulted in a 15% reduction in churn rates, demonstrating the model's effectiveness.

I handle missing data by first identifying the extent and pattern of missingness. If the data is missing completely at random, I might use imputation techniques like mean, median, or mode. For more complex cases, I use machine learning-based imputation methods like K-Nearest Neighbors (KNN) or predictive modeling. If the missing data is not random, I explore the underlying reasons and consider whether to exclude those records or use more sophisticated techniques like multiple imputation. My goal is to minimize bias and maintain data integrity.

To ensure model interpretability, I start by selecting algorithms that are inherently interpretable, such as linear regression or decision trees. For more complex models like neural networks, I use techniques like LIME or SHAP to explain individual predictions. I also focus on feature engineering to ensure that the input variables are meaningful and understandable. Additionally, I document the model's assumptions, limitations, and decision-making process to make it transparent to stakeholders. This approach helps in building trust and ensuring that the model's decisions can be justified.

I stay updated by regularly reading research papers, attending conferences, and participating in online courses and webinars. I follow leading data science blogs and subscribe to journals like 'Nature' and 'IEEE Transactions on Pattern Analysis and Machine Intelligence'. I also engage with professional communities on platforms like LinkedIn and GitHub, where I contribute to open-source projects and discuss the latest trends. This continuous learning helps me apply the latest techniques and tools to my work, ensuring that my skills remain relevant and cutting-edge.

In a project to optimize supply chain logistics, we initially planned to use a deep learning model to predict demand. However, we encountered data quality issues that made the model unreliable. Instead of pushing forward, I advocated for a pivot to a more traditional time-series analysis approach, using ARIMA models. This shift allowed us to leverage historical data more effectively and provided more accurate predictions. The project was ultimately successful, and the lessons learned reinforced the importance of flexibility and adaptability in data science.