Sample Answer
In my last role, I led an end-to-end churn prediction project for our B2B SaaS product using Python. We were seeing roughly 12% quarterly churn in mid-market accounts. I owned the full technical workflow: pulling data from Snowflake with Python, cleaning and feature engineering in Pandas, and training XGBoost models with scikit-learn. I set up a robust pipeline using Airflow and wrote unit tests in pytest to catch data quality issues. I partnered closely with a product manager and a customer success lead to define what “actionable” meant. Together we decided on a weekly churn risk score and top 3 drivers per account. I worked with a backend engineer to expose the model via a FastAPI service and an MLOps engineer to containerize it with Docker and deploy to Kubernetes. Within 3 months, we reduced churn in the targeted segment by 18% and increased expansion revenue by about $600K annually.
Keywords
Walk me through a recent multi-channel digital marketing campaign you managed end-to-end. How did you set objectives, choose channels, allocate budget, and measure success?
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