Key Responsibilities

Data Analysis & Insights

• Analyze large datasets from multiple sources to identify trends, patterns, and actionable insights.
• Support data-driven strategies for subscription growth and user engagement.

Machine Learning & Predictive Modeling

• Design, develop, and deploy statistical models and machine learning algorithms for:
• Subscription propensity modeling
• Churn prediction
• Plan recommendation systems
• Winback strategies
• Apply Generative AI models to enhance user acquisition and personalization.

Production & Automation

• Integrate machine learning models into production systems for real-time insights and automated decision-making.
• Continuously monitor and improve data quality and model performance.

Experimentation & Impact Measurement

• Develop A/B testing frameworks and experimental designs to evaluate algorithm performance.
• Quantify the business impact of models and insights.

Cross-Functional Collaboration

• Partner with Data Engineering, Product Management, Growth, and Business teams to build end-to-end data science solutions.
• Translate business problems into actionable data science solutions.

Communication & Innovation

• Present analytical findings, methodologies, and business impact to technical and non-technical stakeholders.
• Stay updated on the latest advancements in machine learning, AI, and data science.