AI ENGINEER · DATA SCIENTIST
AI Engineer · Data Scientist · Data Engineer
I started in actuarial and payroll roles, moved through reinsurance and banking, and now build AI and data platforms at Ford. My work brings machine learning, data engineering, cloud, and optimization together. I enjoy using data and AI to make complex systems easier to understand and decisions easier to make.
Born in Mexico City · 1997Open to international opportunities
Global Data Insights & Analytics
Credit Intelligence & Decision Science
Data Engineering
Insurance Claims Intelligence
Community Outreach
Payroll Operations
Co-developed and brought Ford’s real-time claims data architecture to production, enabling continuously updated warranty insights.
Expanded warranty claim cost predictions across vehicle lines, model years, parts, and vehicle functions.
Reengineered data pipelines into serverless jobs that automatically scale computing resources to each task.
Increased monthly payroll-loan placement by 42% through data-driven interest-rate discount optimization.
Customers selected by the models had a default rate one-third that of the overall portfolio, and their card contracts accounted for 10% of annual sales.
Remediated more than 10 end-user computing (EUC) processes, including Excel workbooks, Visual Basic tools, manual reports, and Python scripts. Added failure monitoring, data validation, access controls, approvals, and documentation to bring these workflows under greater control.
Helped pioneer real-time claims data processing and bring the architecture to production alongside a colleague. Built production pipelines that orchestrate multi-agent AI workflows and machine-learning models using enriched warranty data. The system continuously classifies claims, surfaces likely causes, and groups related issues into intelligent problem objects, clear groupings of similar claims. This gives Ford’s global plant teams timely, organized insight into issues across vehicles, parts, and vehicle functions.
Modularized production forecasting pipelines to predict warranty claim costs at four levels: vehicle line, model year, part, and vehicle function. The results flow into a dashboard that supports monthly warranty reserve adjustments and annual planning.
Redesigned data pipelines to split serial workloads into smaller, parallel serverless jobs. Each job scales its computing resources to the task, reducing idle capacity and unnecessary processing while supporting more prediction workloads.
Designed and analyzed discount experiments and models to estimate each customer’s likelihood of taking a loan at different interest-rate discounts. Used genetic algorithms to find models with strong predictive performance while making discount size an influential factor in customer decisions. Then used linear programming to select discount levels that increased loan placement while preserving the campaign’s margin.
Led model delivery from feature development through production validation. Developed features capturing customer behavior, transaction activity, and product usage, then applied model scores and risk thresholds to identify lower-risk customers for monthly card offers. Customers selected by the models had a default rate one-third that of the overall portfolio, and their card contracts accounted for 10% of annual sales. Validated the code, coordinated testing, adapted the models to BBVA’s platform, and worked with Data Engineers during parallel production runs and migration validation.
Coordinated the remediation of more than 10 end-user computing (EUC) processes across business teams, including workflows supporting regulatory reporting. Replaced selected Excel, Visual Basic, manual-reporting, and unoptimized Python workflows with standardized Python and SQL solutions. Designed and built replacements directly while guiding users through remediation of other workflows. Added failure monitoring, data validation, access controls, approval steps, and documentation to move these workflows into secure, monitored enterprise operations.
Diploma program · Aug 2021 – Mar 2022
Statistics, probability, and financial risk.
IBM-certified in Data Science.
Overall band score · 7.5
