Could you give an example of how you have applied problem-solving skills in a project?

A recent project where I applied my problem-solving skills involved developing a recommendation system. We noticed that our model's accuracy was subpar, and I figured out that the issue was the data preprocessing step. Specifically, the user-rating data had outliers that skewed the model's predictions. I decided to apply outlier detection methods, removing these extreme values, and then I retrained the model. After that, the model's accuracy improved significantly. This experience taught me the importance of data quality in machine learning.

On my last project, a sentiment analysis tool, I put my problem-solving abilities to the test. The model was performing poorly, and I initiated a thorough review of our preprocessing pipeline. Through iterative analysis, I discovered that the sentiment labels were incorrectly assigned due to a parsing issue during data cleaning. My solution was to refactor the data cleaning script in Python, ensuring proper encoding and handling of text data. Collaborating closely with the data science team, we redesigned the label assignment process to align more accurately with sentiment lexicons. After these adjustments and retraining with the corrected data, the model’s accuracy increased by 12%. We then communicated these improvements to our stakeholders, detailing the problem-solving process and the benefits of the refined model.

In my recent role, I was tasked with optimizing a machine learning model for customer segmentation. The model's performance was suboptimal, and as I delved into the problem, I unearthed that it was due to inadequate feature selection in the preprocessing stage. To tackle this, I utilized Python and scikit-learn to implement a robust feature engineering strategy. Collaborating with our statisticians, I facilitated informative sessions to brainstorm key features that could impact customer behavior. Leveraging my knowledge of statistical analysis, we introduced new features such as customer lifetime value and purchase frequency. Post-revamp, our model saw a performance leap, accuracy elevating by 20%. Through this iterative process, I honed my problem-solving technique, learning to question conventional assumptions and explore data more creatively. I documented the entire workflow, from the problem identification to solution implementation, which served as a vital reference for the team. Engaging in-depth with cross-functional peers, I communicated our progress, capturing feedback to refine our approach. This collaboration led to a deeper integration with the software engineering unit to scale the model deployment effectively.