Vice President, Data Science

S&P Global•Raleigh, NC

5d•$177,036 - $350,000•Remote

About The Position

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. 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. 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.

Requirements

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.
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.
Practical experience designing explainable models suitable for regulated environments, including feature attribution and model transparency techniques.
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, and collaborating closely with software engineers and platform teams.
Pragmatic and outcome-driven; 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.

Nice To Haves

Limited line management responsibility.
May have limited line management responsibility.

Responsibilities

Lead the design, development, and operation of high-rigor analytical and machine-learning systems.
Define the AI/ML roadmap for Enterprise Solutions.
Build high-rigor analytical and predictive models for anomaly detection, variance analysis, drift detection, market and behavioral signals, forecasting, and prediction.
Ensure AI/ML strategy is sound and analytical models are correct, explainable, reliable in production, and able to withstand operational and regulatory scrutiny.
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.
Get involved early in complex or high-risk analytical problems and step in when models degrade or fail in production.
Determine when to apply advanced modelling, when simpler approaches are sufficient, and when modelling is not appropriate.
Provide hands-on technical contribution, review, and influence.
Own the full model lifecycle: data exploration, feature engineering, model development, back-testing, validation, deployment, monitoring, and ongoing tuning.
Operate models in production over time, including versioning, drift detection, retraining strategies, and incident response.
Design explainable models suitable for regulated environments, including feature attribution and model transparency techniques.
Combine statistical models, ML, semantic models, and rules-based logic where needed to achieve accuracy, stability, and explainability.
Focus on data quality, anomaly detection, and monitoring, including metrics that surface real issues and drive sustained improvement.
Act as a technical mentor to other data scientists through review, pairing, and example, with limited people management where appropriate.