S&P Global · 1 day ago
Executive Director -Data Science
New York, NY
Full-time
Onsite
Director/Executive
$177K/yr - $266K/yr
17+ years expS&P Global is a leading provider of data and analytics solutions, and they are seeking an Executive Director of Data Science to lead the design and development of analytical and machine-learning systems. The role involves defining the AI/ML roadmap and ensuring the models are reliable and capable of withstanding regulatory scrutiny while collaborating with various teams to bring models to production.
AnalyticsBusiness IntelligenceCreditEnterprise SoftwareFinanceFinancial ServicesInformation ServicesMarket Research
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, including 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, including metrics that surface real issues and drive sustained improvement
Qualification
Required
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, including 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, including metrics that surface real issues and drive sustained improvement
17+ 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
Acts as a technical mentor to other data scientists through review, pairing, and example, limited people management where appropriate
Benefits
Health & Wellness: Health care coverage designed for the mind and body.
Flexible Downtime: Generous time off helps keep you energized for your time on.
Continuous Learning: Access a wealth of resources to grow your career and learn valuable new skills.
Invest in Your Future: Secure your financial future through competitive pay, retirement planning, a continuing education program with a company-matched student loan contribution, and financial wellness programs.
Family Friendly Perks: It’s not just about you. S&P Global has perks for your partners and little ones, too, with some best-in class benefits for families.
Beyond the Basics: From retail discounts to referral incentive awards—small perks can make a big difference.
Company
S&P Global
S&P Global is a market intelligence company that provides financial information and data analytics services.
10001+ employees
H1B Sponsorship
S&P Global has a track record of offering H1B sponsorships. Please note that this does not
guarantee sponsorship for this specific role. Below presents additional info for your
reference. (Data Powered by US Department of Labor)
Distribution of Different Job Fields Receiving Sponsorship
Represents job field similar to this job
Trends of Total Sponsorships
2025 (28)
2024 (26)
2023 (30)
2022 (38)
2021 (46)
2020 (38)
Funding
Current Stage
Public CompanyTotal Funding
$1.75B2025-12-01Post Ipo Debt· $1B
2023-09-07Post Ipo Debt· $750M
2016-04-28IPO
Leadership Team
Martina Cheung
President and CEO
Rick Goldberg
Division Chief Financial Officer
Recent News
2026-01-23
Benzinga.com
2026-01-23
2026-01-22
Company data provided by crunchbase