Senior Data Scientist who designs, develops, and implements machine learning models, AI systems, and statistical modeling to generate actionable insights for business problems and product development.
Compensation
Pay Range: $108,960.00 - $163,440.00 Annual
Responsibilities
Designs, develops, programs and implements Machine Learning Models (Clustering, Classification or Regressions models).
Performs Statistical Modelling and Measurements by applying data engineering, feature engineering, statistical methods, ML modelling and AI techniques.
Develops and applies novel models on customer data to identify areas to increase profits and reduce losses.
Partners with experimentalists to translate models into testable hypotheses.
Lead and scale a world-class engineering organization of 100+ data engineers, ML engineers, and platform developers, including managing managers and Staff/Principal-level engineers while driving hiring, retention, and talent development strategies
Compensation
(This section is only applicable to US candidates) S&P Global states that the anticipated base salary range for this position is $185,000 to $265,000. Final base salary for this role will be based on the individual’s geographic location, as well as experience level, skill set, training, licenses and certifications. In addition to base compensation, this role is eligible for an annual incentive plan. This role is not eligible for additional compensation such as an annual incentive bonus or sales commission plan.
Responsibilities
Lead and scale a world-class engineering organization of 100+ data engineers, ML engineers, and platform developers, including managing managers and Staff/Principal-level engineers while driving hiring, retention, and talent development strategies
Provide strategic technical direction for enterprise-scale lakehouse architecture using tools such as Databricks Data Intelligence Platform (Unity Catalog, Delta Lake, Delta Live Tables, Mosaic AI) and AWS cloud services, ensuring highly performant, cost-effective, and interoperable data foundations
Drive the organization's transition to production-grade autonomous AI systems, leveraging tools such as Databricks AI stack including MLflow, Feature Store, Vector Search, and Model Serving to deliver measurable business value beyond proof-of-concept implementations
Establish rigorous engineering standards and operational excellence practices including SLOs, error budgets, incident management, and infrastructure-as-code using tools such as Terraform and Databricks Asset Bundles for reliable platform delivery
Lead and scale a world‑class engineering organization of 100+ data engineers, ML engineers, and platform developers, providing strategic technical direction for enterprise‑scale lakehouse architecture and autonomous AI systems.
Compensation
(This section is only applicable to US candidates) S&P Global states that the anticipated base salary range for this position is $185,000 to $265,000. Final base salary for this role will be based on the individual’s geographic location, as well as experience level, skill set, training, licenses and certifications.
Responsibilities
Lead and scale a world‑class engineering organization of 100+ data engineers, ML engineers, and platform developers, including managing managers and Staff/Principal‑level engineers while driving hiring, retention, and talent development strategies
Provide strategic technical direction for enterprise‑scale lakehouse architecture using tools such as Databricks Data Intelligence Platform and AWS cloud services
Drive the organization’s transition to production‑grade autonomous AI systems leveraging Databricks AI stack
Establish rigorous engineering standards and operational excellence practices including SLOs, error budgets, incident management, and infrastructure‑as‑code
Oversee enterprise semantic modeling and RAG architecture strategies
Oversee enterprise semantic modeling and RAG architecture strategies, ensuring AI systems are grounded in Unity Catalog-governed data assets with proper access controls, lineage tracking, and auditability to prevent hallucinations
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