Customer Churn Prediction Project – Data Scientist / Machine Learning Engineer Needed

I am seeking an experienced Data Scientist or Machine Learning Engineer to complete an end-to-end customer churn prediction project using a provided dataset. The objective is to develop a robust machine learning solution that identifies customers who are likely to churn, uncovers the key factors driving churn, and provides actionable business recommendations for improving customer retention. The project should include data cleaning and preprocessing, exploratory data analysis (EDA), feature engineering, model development, model evaluation, and interpretation of results. I would like multiple classification models to be built and compared, including Logistic Regression, Random Forest, XGBoost, LightGBM extra. The models should be evaluated using appropriate metrics such as Accuracy, Precision, Recall, F1-Score, and ROC-AUC, with particular attention given to identifying customers at high risk of churn. In addition to building predictive models, I would like a detailed analysis of the factors that contribute most to customer churn and clear business recommendations based on the findings. The final deliverables should include a well-documented Jupyter Notebook, cleaned dataset, source code, model files, visualizations, and a comprehensive report summarizing the methodology, results, model comparison, feature importance analysis, and recommendations. When applying, please provide examples of similar machine learning, predictive analytics, customer churn, or classification projects you have completed. Experience with Python, pandas, scikit-learn, XGBoost, LightGBM, data visualization, and business-focused analytics is highly preferred. This is a one-time fixed-price project with a defined scope and deliverables.