About the Role
Grade Level (for internal use): 15
The Enterprise Solutions Technology team delivers next-generation, high‑scale technology platforms for regulated financial services. This role is a Data Scientist Leader responsible for designing, developing, and operating high‑rigor analytical and machine‑learning systems across a complex, regulated estate.
Responsibilities
- Lead the design, development, and operation of enterprise‑grade analytical and predictive models for anomaly detection, variance analysis, drift detection, market and behavioural signals, forecasting, and prediction.
- Define and drive the AI/ML roadmap for Enterprise Solutions.
- Translate business problems into production‑ready models through feature engineering, back‑testing, deployment, monitoring, and performance management.
- Collaborate with engineering, data platform, and product teams to move models from concept to production.
- Respond to model degradation or failure in production and implement remedial actions.
- Evaluate when advanced modelling is required versus simpler approaches, and when modelling is not appropriate.
- Own the full model lifecycle: data exploration, feature engineering, model development, back‑testing, validation, deployment, monitoring, and ongoing tuning.
- Operate models over time, managing versioning, drift detection, retraining strategies, and incident response.
- Design explainable models suitable for regulated environments, including feature attribution and transparency techniques.
- Integrate statistical models, ML, semantic models, and rules‑based logic to achieve accuracy, stability, and explainability.
- Maintain data quality, anomaly detection, and monitoring systems that surface real issues and drive sustained improvement.
Qualifications & Experience
- 25+ years of experience with analytics, data science, or ML systems in production within financial services or other regulated, high‑availability domains.
- Strong background in statistics, machine learning, time‑series analysis, and predictive modelling.
- Data‑driven experience working with large, complex, and imperfect datasets (missing data, outliers, regime changes, noisy labels, evolving schemas).
- Deep understanding of production ML system design, including batch vs real‑time inference and model serving patterns.
- Experience operating models in production over time, including versioning, drift detection, retraining, and incident response.
- Practical knowledge of explainable models for regulated environments (feature attribution, transparency techniques).
- Strong focus on data quality, anomaly detection, and monitoring with metrics that drive improvement.
- Comfortable collaborating with software engineers and platform teams, and communicating modelling choices to engineers, product partners, and senior stakeholders.
- Experience mentoring junior data scientists through review, pairing, and example.
Benefits
- Health & Wellness: comprehensive health‑care coverage.
- Flexible Downtime: generous time‑off policies.
- Continuous Learning: access to training resources and a company‐matched student‑loan contribution.
- Retirement Planning: competitive pay, retirement plans, and financial wellness programs.
- Family Friendly Perks: family‑focused benefits and discounts.
- Additional Perks: retail discounts, referral incentives, and other employee perks.
Equal Opportunity Employer
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. If you need an accommodation during the application process due to a disability, please email EEO.Compliance@spglobal.com.
