AI Strategy Vice President

BBVA•New York, NY

3d•$150,000 - $190,000•Onsite

About The Position

BBVA’s US AI & Data Team accelerates business transformation by turning data and artificial intelligence into trusted, scalable capabilities that deliver measurable value. The team works with business, technology, risk, and operations groups to identify high-impact opportunities, develop and deploy data-driven and AI-enabled solutions, and support responsible adoption through data quality, governance, explainability, and performance monitoring. The AI Strategy Vice President will lead the design, development, and industrialization of data science and AI solutions to improve client engagement, commercial effectiveness, operational efficiency, and risk management. The role combines analytical expertise and business judgment to translate strategic priorities into scalable, governed, measurable data products. It also supports portfolio prioritization and adoption through stakeholder collaboration, training, and change management.

Requirements

At least 7 years of professional experience as a Data Scientist, Analytics Specialist, Machine Learning Engineer, or in a comparable data-focused role.
Demonstrated experience delivering end-to-end analytical or AI solutions in complex, cross-functional environments, from problem definition through production deployment and value tracking.
Strong hands-on skills in Python and SQL, statistical analysis, machine learning, experimentation, and data visualization.
Ability to work with large, imperfect data sets and communicate technical concepts clearly to non-technical audiences.
Bachelor's or Master's degree in Data Science, Statistics, Computer Science, Engineering, Mathematics, Economics, or a related quantitative discipline.
Professional fluency in English.
Eligibility to work in the U.S.; BBVA will not pursue visa sponsorship for this position.

Nice To Haves

Experience with cloud data platforms and MLOps is strongly valued.
Experience in financial services, regulated environments, or risk-sensitive use cases is an advantage.

Responsibilities

Frame high-value business problems and define data science approaches, success criteria, and measurable KPIs with business, product, and operations stakeholders.
Build, validate, and deploy predictive, optimization, and machine learning models; contribute to Generative AI and agent-based use cases where appropriate.
Translate data insights into clear recommendations and decision-ready narratives for senior stakeholders, including assumptions, limitations, and expected impact.
Partner with Data for AI and AI Tech, Risk, Compliance, and Operations to deliver reusable, scalable solutions and manage dependencies across initiatives.
Establish model documentation, monitoring, and performance standards to ensure accuracy, explainability, responsible AI, and compliance with data and AI governance.
Support portfolio prioritization by assessing feasibility, data readiness, investment needs, value potential, and adoption requirements.
Promote solution uptake through training and change management.