Vice President, Data Science

About the Role:

Data Scientist Leader to design, develop, and operate high‑rigor analytical and machine‑learning systems across a complex, regulated financial‑services estate.

• Lead the AI/ML roadmap for Enterprise Solutions and build production‑grade analytical and predictive models for anomaly detection, variance analysis, drift detection, market and behavioral signals, forecasting, and prediction.
• Run models from problem definition through production operation, including feature engineering, back‑testing, deployment, monitoring, and ongoing performance management.
• Deploy advanced modelling when appropriate, but also know when simpler approaches or no modelling are sufficient; act when models degrade or fail in production.
• Provide technical guidance, code reviews, and mentorship to other data scientists; limited line‑management responsibilities.

• Strong experience delivering applied data science and machine learning in production within banking, capital markets, or similarly regulated, data‑intensive environments.
• Deep grounding in statistics, machine learning, time‑series analysis, and predictive modelling, with experience building models under real operational constraints.
• Hands‑on ownership of the full model lifecycle: data exploration, feature engineering, model development, back‑testing, validation, deployment, monitoring, and ongoing tuning.
• Extensive experience working with large, complex, and imperfect datasets, including missing data, outliers, regime changes, noisy labels, and evolving schemas.
• Strong understanding of production ML system design, including batch vs real‑time inference, model serving patterns, performance trade‑offs, and failure modes.
• Experience operating models in production over time: versioning, drift detection, retraining strategies, and incident response when models misbehave.
• Practical experience designing explainable models suitable for regulated environments, including feature attribution and model transparency techniques.
• Experience combining statistical models, ML, semantic models, and rules‑based logic where needed to achieve accuracy, stability, and explainability.
• Strong focus on data quality, anomaly detection, and monitoring, with metrics that surface real issues and drive sustained improvement.
• 25+ years working with analytics, data science, or ML systems in production, with significant experience in financial services or other regulated, high‑availability domains.
• Comfortable working directly with data, models, and code, collaborating closely with software engineers and platform teams.
• Pragmatic, outcome‑driven, and measures success by models that run reliably in production, adapt to changing conditions, and withstand scrutiny.
• Clear communicator who can explain modelling choices, assumptions, and limitations to engineers, product partners, and senior stakeholders.
• Acts as a technical mentor to other data scientists through review, pairing, and example; limited people management where appropriate.

Base salary range: $177,036 to $350,000, depending on geographic location, experience level, skill set, training, licenses, and certifications.

Eligible for an annual incentive plan and additional S&P Global benefits.

S&P Global is an equal opportunity employer and all qualified candidates will receive consideration for employment without regard to race/ethnicity, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, marital status, military veteran status, unemployment status, or any other status protected by law. Only electronic job submissions will be considered for employment.