Martin Alvarez

AI/ML consultant delivering production solutions for global clients across LegalTech, FinTech and conversational AI. Designed recommendation systems, GenAI/RAG pipelines, agentic AI assistants and churn models from experimentation through deployment. Worked across AWS, ML engineering and data pipelines, improving model performance, reducing processing and LLM costs, and supporting customer retention and cross-sell initiatives at large scale.

Freelance Data Scientist & AI Engineer delivering end-to-end solutions for international clients. Built data pipelines, web scraping and RAG systems powering a U.S. credit union loan-matching platform, combining public financial data with AI-extracted product information. Also developed executive Power BI analytics solutions, translating sales and operational data into actionable business insights.

Data Scientist developing end-to-end ML and data products for e-commerce and pharmaceutical operations. Built churn, recommendation and demand forecasting systems, translating customer and market data into retention, pricing and inventory decisions. Delivered measurable impact including 8% lower churn, 35% fewer stockouts, higher AOV and gross margin, while also improving production services and data quality at scale.

Power BI Developer supporting U.S. logistics clients with dashboards and automated reporting for sales, operations and workforce performance. Built Python ETL pipelines to replace manual CRM data entry and designed business-focused KPIs for decision-making. Delivered dashboards directly to customers and presented solutions in demo sessions, contributing to improved sales performance and operational visibility.

• Improved On-Time Delivery Rate from 89% to 95% by developing a data pipeline in Python that provided high-frequency visibility and alerts of freight status. • Develop ETL pipelines to receive and store transactional data from the logistics industry. • Submit periodical reports based on operational results for transportation carriers.

• Maintained Customer Satisfaction Score (CSAT) above 80% through effective monitoring of the operation. • Executed complex SQL queries for customer data retrieval and database population. • Developed PowerBI dashboards to centralize key customer service KPIs, such as Average Response Time, Customer Satisfaction Score (CSAT) and First Contact Resolution (FCR). resulting in an improvement in service efficiency. • Analyzed trends in customer queries and warranty claims data, using these insights to inform imp