Predictive Modeling | Python | BI | Data Science
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As Head of Asset Risk at LeasePlan UK, I lead a team responsible for asset risk assessment and forecasting, working closely with the second line of defense to develop new methodologies for supply and demand estimation and secondhand market analysis. I successfully implemented a new pricing methodology for various vehicle and fuel types, expanding the number of models from 4 to 32 and reducing the process time by 60%, from 90 days to 30. Additionally, I played a key role in developing in-house forecasting models using Python and machine learning techniques, which significantly improved the accuracy of used car price and maintenance projections.
In my previous role as Head of Risk Management at LeasePlan Mexico, I was responsible for key areas such as Asset Risk, Credit Risk, Operational Risk, Treasury Risk, and Information Security, leading a team of seven professionals. I developed strategies using machine learning models to improve risk forecasting and decision-making. I also optimized income lines across contract lifecycles, enhancing profitability, and mitigating MXN 20 million in credit notes, contributing to the overall financial stability of the company.
With extensive experience in risk management, I contributed to the development of tools that reduced reporting times from 20 days to just 2 hours, improving decision-making processes. My expertise in Python, SAS Enterprise, and Business Intelligence, combined with my focus on sales and machine learning, has enabled me to drive innovation in pricing models for repair and maintenance services, achieving a 50% reduction in costs for electric vehicle budgets.
Fluent in both English and Spanish, I bring a proven track record in risk management, operational efficiency, and strategic asset assessment within the automotive and leasing industries. I am now looking to leverage my skills in analytics, sales, and machine learning in a dynamic environment that values innovative risk management and data-driven decision making.
International experience with over 2 years in the United Kingdom and over a month in the Netherlands.