Vice President, Data Science

About the role

Grade Level (for internal use): 15 The Team The Enterprise Solutions Technology team is dedicated to delivering next-generation, high-scale technology platforms through resilient architecture, data excellence, and engineering innovation. Our mission is to enhance our digital presence and improve customer engagement across various domains, including Lending, Corporate Actions, Tax, Regulatory & Compliance, Regulatory Reporting, Public Markets, and Private Markets portfolio monitoring. Role We are seeking a Data Scientist Leader to lead the design, development, and operation of high-rigor analytical and machine-learning systems across a complex, regulated financial-services estate. This is a strategy-led and hands-on applied data science and ML engineering role, responsible for defining the AI/ML roadmap for Enterprise Solutions while also building high-rigor analytical and predictive models for anomaly detection, variance analysis, drift detection, market and behavioral signals, forecasting, and prediction. The expectation is production-grade models, comparable in rigor to fraud, risk, or surveillance systems. Compensation/Benefits Information: (This section is only applicable to US candidates) S&P Global states that the anticipated base salary range for this position is $2,07060 to $3,53,063. Final base salary for this role will be based on the individual's geographic location, as well as experience level, skill set, training, licenses and certifications. In addition to base compensation, this role is eligible for an annual incentive plan. This role is not eligible for additional compensation such as an annual incentive bonus or sales commission plan.This role is eligible to receive additional S&P Global benefits. For more information on the benefits we provide to our employees, please click here What's In for you : The role exists to ensure AI/ML strategy is sound and that analytical models are correct, explainable, reliable in production, and able to withstand operational and regulatory scrutiny. You will work closely with engineering, data platform, and product teams to take models from problem definition through to production operation, including feature engineering, back-testing, deployment, monitoring, and ongoing performance management. You will get involved early in complex or high-risk analytical problems and step in when models degrade or fail in production. A key part of the role is knowing when to apply advanced modelling, when simpler approaches are sufficient, and when modelling is not appropriate . You may have limited line management responsibility, but impact is driven primarily through hands-on technical contribution, review, and influence.