Senior AI Quant

BBVA is a global company with over 160 years of history, present in 25 countries, and over 81 million customers. We have more than 120,000 professionals working in multidisciplinary and diverse teams.

At BBVA, we are ahead of the transformation in the banking sector, challenging the status quo to make life easier for our customers.

Being part of BBVA means developing your career in one of the most innovative companies in finance.

About the role:

Advanced Analytics & Algorithmic Trading is the team of Data Scientists within C&IB Global Markets, focused on developing trading algorithms, business intelligence tools, and advanced analytics solutions for trading and sales teams. The team blends strong technical expertise with a deep understanding of financial markets to deliver innovative, high-impact solutions.

We are seeking an exceptional Senior AI Quant (VP/ED) to lead research and prototyping of advanced AI solutions within Global Markets, with an initial focus on risk management for equity structured products. This is a strategic leadership role driving the next generation of modeling capabilities that will reshape how Global Markets prices, hedges, and manages risk.

This role sits embedded within Global Markets to ensure strong connectivity with the business units. You will lead the research stream—focused on ideation, prototyping, and validation—while partnering closely with the execution teams who industrialize the most promising AI models. Both teams report jointly to the Head of the Advanced Analytics & Algorithmic Trading team, ensuring alignment between research excellence and business impact.

Key Responsibilities:

• Develop AI-driven predictive models for market risk factors relevant to structured products (volatility surfaces, correlation, skew dynamics, liquidity indicators, etc.).
• Prototype optimization models for dynamic hedging, incorporating RL, deep learning, and advanced numerical techniques.
• Research efficient AI-based estimators for pricing and risk metrics (Greeks, scenario computations, exposures), including neural approximators and surrogate models
• Explore reasoning models and agent-based architectures capable of supporting trading decision workflows.
• Investigate fine-tuning and alignment of large language models (LLMs) to act as safe, reliable trading agents or risk copilots.

Qualifications

Academic & Technical Background

• Master’s or PhD in Physics, Mathematics, Engineering, Computer Science, Statistics, or a related quantitative field.
• 7–12+ years of experience in quantitative research, systematic trading, financial AI, or pricing/risk model development in financial markets.
• Deep knowledge of derivatives and financial markets, ideally with direct exposure to equity structured products.

Strong foundations in:

• Stochastic processes
• Numerical PDE/SDE methods
• Optimization and optimal control
• Machine learning / deep learning (PyTorch, TensorFlow, JAX, scikit-learn)
• Reinforcement learning and sequential decision models

Programming Skills

• Expert-level Python is mandatory.
• Experience with KDB+/Q, C++, Java or GPU acceleration is a plus.
• Familiarity with modern ML toolchains

Soft Skills & Cultural Fit

• Strong ability to operate in a front-office-adjacent, high-pressure environment with tight feedback loops.
• Entrepreneurial mindset, able to independently identify high-impact AI opportunities.
• Excellent communication skills: able to interface with traders yet also produce rigorous research output.
• Fluent English required; Spanish welcome but optional.
• Passion for cutting-edge AI innovation and its safe application in financial markets.

What We Offer

• A pivotal VP/ED leadership role shaping the future of AI in Global Markets.
• A high-energy environment where research rapidly becomes production.
• The opportunity to build novel AI models that directly influence pricing, hedging, and risk management at scale.
• A highly collaborative team combining quantitative finance, advanced analytics, machine learning research, and algorithmic trading.