Ali – Data Science, SQL, Python

A large-scale public sector initiative to modernize public safety and infrastructure operations through AI-driven automation and reporting. The project focused on digitizing manual workflows (including OCR-based billing forms) and implementing automated reporting and validation frameworks, improving data accuracy, compliance, and delivery speed.

• Strategic AI Automation: Led stakeholder-driven initiatives to automate manual workflows, including OCR-based billing form digitization, improving project manager experience, and NPS;
• Reporting Automation: Primary POC for building an automated Microsoft Access solution for UE DARP reporting, cutting delivery timelines by ~50%;
• Install Form Validation: Led development of an automated framework to validate UE/DNF installation forms against CMDB data, improving accuracy and compliance;
• Data Warehousing: Designed a centralized Microsoft Access data warehouse to replace file-based systems, improving data retrieval efficiency by ~70%.

An end-to-end visibility initiative in a global B2B e-commerce marketplace focused on improving supplier behavior and operational efficiency by implementing web tracking, building a scalable data warehouse, and leveraging agentic AI to identify interaction pain points. This resulted in reduced cancellations and improved customer service response times.

• Reduced supplier ticket review volume by 34% by building an end-to-end visibility system and improving supplier behavior insights;
• Developed an agentic AI model using Google Gemini to identify top supplier pain points, enabling targeted operational improvements;
• Improved customer service response times by driving data-backed interventions across supplier workflows;
• Enhanced supply chain transparency by implementing end-to-end tracking and analytics, improving supplier-customer interactions;
• Enabled data-driven decision-making through centralized data pipelines and actionable insights for operations teams.

A large-scale real estate marketplace initiative serving over 164,000 realtors and brokers, focused on improving efficiency and customer engagement through AI/ML-driven property recommendations, customer segmentation and churn analysis, and data-driven buyer–agent matchmaking, resulting in improved lead generation and transaction outcomes.

• AI/ML-Based Recommendation Engine: For over 20,000 active listings, conducted advanced data analysis, item-based & collaborative filtering in Numpy & Pandas for a Recommendation engine project to improve customer engagement in real estate listings within Texas;
• Customer Segmentation & Churn analysis: With a team of 2 analysts, utilized Python & Tableau to enhance understanding of user behavior to derive impactful marketing strategies, leading to a 20% increase in leads on the website;
• Marketplace Match Making: Utilized complex SQL queries to match the right agents with customers based on their preferences, achieving 35% improvement in marketplace health and completed transactions.

A leading MENA super app initiative focused on enhancing ride-hailing and marketplace operations through data-driven solutions and scalable infrastructure.

• Improved ETA prediction models across KSA & UAE, achieving a 15% reduction in late orders and increasing ETA accuracy by 27 percentage points using a combination of machine learning techniques, including XGBoost, Random Forest, linear regression, and decision trees;
• Worked with large-scale datasets (1PB+), leveraging SQL for deep analysis and building comprehensive Tableau dashboards to monitor marketplace health and mobility KPIs across the Gulf region;
• Designed and conducted A/B tests to evaluate and optimize rerouting strategies during rides, leading to a 2% reduction in cancellations;
• Collaborated closely with Product Managers in an Agile environment to continuously improve marketplace efficiency, focusing on better matchmaking between supply and demand and reducing overall ETAs;
• Mentored and trained cross-functional team members in data analytics best practices, promoting a culture of data-driven decision-making and enabling more informed business strategies.

A financial analytics initiative focused on delivering data-driven solutions for major lending clients (including HSBC, Toyota, Sallie Mae, and Upgrade) by building executive dashboards, modernizing data infrastructure with cloud-based ETL pipelines, and implementing predictive fraud detection models, enabling faster insights, better decision-making, and improved operational efficiency.

• Delivered executive dashboards in Power BI & Tableau for clients like Toyota, HSBC, Citi, and Sallie Mae, supporting investor pitches and business growth;
• Reduced data retrieval time by 73% by migrating 500GB+ data from offline sources to Snowflake via optimized ETL pipelines;
• Automated reporting workflows using Snowflake schedulers, improving pipeline reliability and reducing manual effort;
• Built ML-based fraud detection models using time-series forecasting, enhancing risk identification for credit and investment portfolios.