Project Detail

Customer Engagement Optimization with ML Models

Built predictive machine learning models to prioritize customers for marketing campaigns, improving targeting decisions within business and regulatory constraints.

Role
Data Scientist
Context
Telecommunications
Domain
AI Applications
Duration
14 months
Status
Finished

What changed

  1. Developed machine learning models to identify customers most likely to respond to specific campaigns, supporting more targeted outreach within regulatory contact limits.

  2. Built custom pipelines for feature engineering and hyperparameter tuning on customer datasets.

  3. Deployed and monitored machine learning workflows in a production environment.

  4. Integrated predictive outputs into campaign selection workflows, enabling day-to-day operational use of the models.

Technology

Python

6 technologies

pandas seaborn PySpark scikit-learn Jupyter Notebook pytest

Machine Learning

2 technologies

Random Forest XGBoost

Databricks

2 technologies

Databricks Clusters MLflow

SQL

3 technologies

PL/SQL HiveQL Spark SQL

Tableau

1 technology

Tableau Desktop

Signals

machine-learning data-science customer-analytics mlops