Data Engineer

Applied Machine Learning Conference; Time (IL); On-site; USD 170,000 - 260,000; Full time

Job summary

S&P Global is seeking a Data Science leader to own the AI/ML roadmap and oversee the design, development, and production operation of high-rigor analytical and machine learning systems across a regulated financial-services estate.

The role involves defining the AI/ML strategy, managing the full model lifecycle, and delivering production-grade models for anomaly detection, forecasting, and market signals, with a strong emphasis on explainability in a regulated environment.

Qualifications

• 20+ years of analytics, data science, or ML systems in production.
• Significant experience in financial services or similarly regulated domains.

Responsibilities

• Own AI/ML roadmap and full model lifecycle from problem definition to retraining.
• Build production-grade models for anomaly detection, forecasting, and market signals with rigorous evaluation.
• Collaborate with engineering, data platform, and product teams; address high-risk analytical problems and model degradation.
• Design explainable models suitable for regulated environments using statistics, ML, semantic models, and rules.
• Lead and mentor teams, contributing deep domain expertise in analytics and ML for regulated settings.

Skills

AI/ML strategy

Model lifecycle

Production ML

Anomaly detection

Forecasting

Explainable models

Regulated environments

Cross-functional collaboration

Back-testing

Job description

S&P Global's Enterprise Solutions Technology team is seeking a Data Science leader to own the AI/ML roadmap and lead the design, development, and production operation of high-rigor analytical and machine learning systems across a complex, regulated financial-services estate — spanning lending, corporate actions, tax, regulatory reporting, and public and private markets.

• Define the AI/ML strategy and own the full model lifecycle: problem definition, feature engineering, back-testing, deployment, monitoring, drift detection, and retraining
• Build production-grade models for anomaly detection, variance analysis, forecasting, and market and behavioral signals — comparable in rigor to fraud, risk, or surveillance systems
• Work closely with engineering, data platform, and product teams; step in on complex or high-risk analytical problems and when models degrade in production
• Design explainable models suitable for regulated environments, combining statistical models, ML, semantic models, and rules-based logic as appropriate
• 20+ years of experience with analytics, data science, or ML systems in production, with significant experience in financial services or similarly regulated domains