Description

You will design, develop, and program systems to deploy ML models to predict customer churn risk.

Responsibilities

• Design, develop, and program methods to deploy ML models (e.g., logistic regression, XGBoost) to predict churn risk.
• Generate actionable insights by translating complex model outputs into business-friendly insights for retention strategies.
• Engineer features from raw customer data, developing metrics for tracking engagement and activity.
• Develop software programs and automated processes to cleanse, integrate, and evaluate datasets for segmentation and model training.
• Extract meaningful insights from data and communicate findings to optimize churn prediction models using metrics like AUC and lift.

Required Skills

• Minimum of 5 years of experience in data science or data engineering with expertise in deploying predictive analytics models.
• Proficiency in Python and SQL.
• Strong understanding of data engineering concepts, including data lakes, columnar formats, ETL tools, and BI tools.
• Demonstrated skill in statistical modeling for churn analysis and customer segmentation (cohort analysis, demographic/firmographic data).
• Experience building, evaluating, and deploying machine learning models.
• Bachelor's Degree in Computer Science, Data Analytics, Engineering, or related field (Master's/Ph.D. welcome).
• Strong critical thinking and autonomous problem-solving capacity.
• Effective collaboration skills within a team environment.

Key Skills

Education

Any Graduate