Enhancing Data Privacy and Predictive Analytics Capabilities
Client Challenge
Careem, a subsidiary of Uber, faced two critical challenges in their operations. First, they needed a secure and efficient way to pseudonymize sensitive customer data before sharing with third-party partners while maintaining data utility. Second, they sought to evaluate and potentially improve their existing machine learning models for key business metrics including supply/demand forecasting, customer lifetime value calculation, and fraud detection systems.
Solution
Our team, with Ahmed as the lead developer, delivered tailored solutions to address both challenges:
Data Privacy Enhancement
We developed a specialized pseudonymization script for Careem that effectively anonymized sensitive customer information while preserving the analytical value of the data. Key aspects included:
- Implementation of industry-standard encryption and data masking techniques
- Creation of consistent pseudonyms that maintained relational integrity across datasets
- Development of a verification system to ensure complete removal of personally identifiable information
- Rapid deployment within a 7-day timeframe to meet urgent business needs
The script was built following Clean Code design principles, ensuring maintainability, readability, and efficient performance even when processing large volumes of data.
Advanced Analytics Collaboration
In partnership with Careem’s data science team, we conducted comprehensive evaluations of their predictive models:
- Performed in-depth analysis of supply and demand prediction models using historical datasets
- Developed alternative CLTV calculation methodologies and benchmarked against existing approaches
- Created and tested enhanced fraud detection algorithms with improved accuracy rates
- Provided thorough documentation of all methodologies and findings for knowledge transfer
Results
- 100% compliance with data privacy regulations for third-party data sharing
- 7-day delivery of the pseudonymization script, exceeding timeline expectations
- 17% improvement in fraud detection accuracy with new model recommendations
- More precise CLTV calculations leading to better customer retention strategies
- Enhanced supply/demand prediction resulting in optimized resource allocation
The collaboration demonstrated how specialized technical expertise could rapidly address both compliance requirements and analytical capabilities for a major ridesharing platform. The pseudonymization script continues to serve as a critical component in Careem’s data sharing infrastructure, while insights from the analytical models have informed strategic business decisions.