Jose Gabriel Orosco Jiménez  new_releases

I am an Economist and Innovation & Data Economics professional specializing in quantitative finance, macro-financial modeling, financial risk, and AI-driven analytical architectures. My profile sits at the intersection of economics, quantitative modeling, financial engineering, risk management, and artificial intelligence, with a strong focus on understanding complex financial systems under uncertainty, non-linearity, regime changes, and extreme market conditions.

My quantitative background combines advanced econometrics, probability and statistics, stochastic modeling, Machine Learning, and computational methods to analyze financial markets, credit portfolios, and macro-financial dynamics. I am particularly interested in problems where traditional linear or static frameworks become insufficient: tail risk, structural breaks, nonlinear dependencies, endogenous feedback mechanisms, changing volatility regimes, and the interaction between macroeconomic conditions and financial risk.

In financial risk, I have experience in model validation, credit and financial risk analytics, derivatives valuation, provisioning methodologies, and stress testing, including work within SFC and IFRS 9 frameworks. My approach goes beyond regulatory implementation: I focus on understanding the statistical assumptions, model risk, calibration, stability, sensitivity, backtesting, and economic interpretation underlying quantitative risk frameworks. This allows me to connect regulatory requirements with rigorous quantitative modeling and portfolio-level decision making.

From a quantitative finance perspective, my interests and capabilities span derivatives pricing, stochastic processes, volatility modeling, Monte Carlo simulation, scenario generation, stress testing, portfolio risk, and the analysis of extreme events. I am particularly interested in extending classical financial models through probabilistic modeling, Machine Learning, Neural SDEs, regime-switching methods, Bayesian inference, and other modern computational approaches capable of representing complex market dynamics.

My work in macro-financial modeling focuses on the transmission of macroeconomic shocks into financial markets, credit risk, liquidity, asset prices, and portfolio performance. I approach these systems through econometric and probabilistic frameworks, combining time-series analysis, causal inference, forecasting and nowcasting, scenario analysis, and nonlinear modeling to identify early-warning signals and understand how risk propagates across economic and financial systems.

I also approach Artificial Intelligence from an architectural rather than purely application-oriented perspective. I am interested in designing quantitative AI systems in which data pipelines, statistical models, Machine Learning, probabilistic inference, simulation engines, decision layers, monitoring, and human oversight operate as an integrated analytical architecture. This includes the design of AI-enabled risk engines, quantitative decision systems, model orchestration pipelines, alternative-data frameworks, digital twins, and architectures that combine econometric reasoning with modern Machine Learning.

This combination gives me a distinctive profile as a quantitative economist capable of moving across three levels of analysis: economic systems, mathematical and statistical models, and the technological architectures required to operationalize them. I can therefore approach a financial problem not only by building or validating a model, but also by understanding its economic foundations, identifying its statistical limitations, designing the surrounding analytical architecture, and translating its outputs into risk and strategic decisions.

My areas of specialization and continued development include quantitative risk management, credit risk, macro-financial modeling, derivatives and capital markets, econometrics, stochastic modeling, Machine Learning for finance, probabilistic modeling, stress testing, model risk management, quantitative AI architectures, Fintech, and data-driven financial decision systems.

Alongside my professional experience, I have independently developed and executed five research streams focused on advanced quantitative and economic problems. These initiatives reflect a strong research orientation and intellectual independence, particularly around nonlinear financial dynamics, systemic and portfolio risk, modern stochastic modeling, and the application of advanced AI methods to economics and finance. I have also led the training and development of other students, strengthening my ability to translate technically complex concepts into structured analytical frameworks and to provide technical leadership.

My long-term trajectory is toward senior quantitative and strategic leadership at the intersection of economics, financial risk, capital markets, and Artificial Intelligence. I aim to develop into a professional capable of architecting quantitative systems, leading sophisticated risk and research functions, and translating advanced mathematical, economic, and AI methodologies into robust financial decisions.

I am particularly interested in environments such as Fintech, Capital Markets, quantitative finance, financial institutions, and Venture Capital, where uncertainty is high, information is imperfect, and superior analytical frameworks can materially improve risk identification, capital allocation, investment decisions, and strategic execution.