Senior Applied AI Data Scientist

Your Opportunity At Schwab, you're empowered to make an impact on your career. Here, innovative thought meets creative problem solving, helping us "challenge the status quo" and transform the finance industry together. This position is M-F during standard business hours with a hybrid work model (4 days in-office, 1 day working from home). It is only available in the areas listed. Candidate must reside or be willing to relocate on their own to one of the listed areas. Applicants must be currently authorized to work in the United States on a full-time basis without employer sponsorship. Retail Supervision & Risk Management is building AI-enabled supervision capabilities that help supervisors identify risk, synthesize evidence, and accelerate consistent, well-documented decisions. In this role, you will lead the development of a portfolio of domain-specific supervision models aligned to discrete risk categories (e.g., documentation review, call transcript risk detection, investor profile vs recommendation discrepancies, and representative activity patterns). You will partner closely with a supervision product portfolio owner, supervision SMEs, and model risk stakeholders to ensure solutions are accurate, explainable, auditable, and operationally sustainable, with supervisors firmly in the loop. This role is expected partner closely with engineering teams to deliver models and controls that are exam-defensible and auditable. In this role you'll – Build a scalable "model factory " Lead architecture and delivery of deployable AI systems Evaluation, controls, and defensibility Documentation and model risk artifacts Data readiness + access patterns Operational controls and continuous improvement Platform execution model Team influence What You Have Required qualifications: 7+ years in applied data science / applied ML with demonstrated ownership of end-to-end model development (problem framing → data → modeling → evaluation → evidence packages). Hands-on proficiency with Python, SQL, and version control (Git); experience writing well-organized, maintainable, reproducible analysis/modeling code. Strong understanding of applied ML evaluation tradeoffs (false negatives vs false positives, calibration, thresholding) and the ability to translate supervisory risk into testable acceptance criteria. Experience building rigorous evaluation and testing approaches (holdouts, error analysis, slice-based performance, stability tests) and defining monitoring/drift indicators. Ability to produce clear, defensible documentation artifacts (model cards/whitepapers, evaluation reports, monitoring definitions) and explain tradeoffs to non-technical partners. Bachelor's degree in a quantitative field (e.g., Statistics, Mathematics, Computer Science, Physics, Engineering, Chemistry, Economics) or equivalent practical experience. Ownership mindset and ability to deliver independently in ambiguous environments; comfortable partnering with engineering/architecture to ship solutions. Preferred qualifications: NLP/LLM familiarity (embeddings, classification, retrieval, prompt/eval patterns); experience designing evaluation and measurement strategies for LLM/RAG outputs in human-in-the-loop workflows. Experience in regulated environments (financial services preferred), including familiarity with audit logging, defensibility, and governance expectations. Hands-on experience with platforms/environments such as Dataiku and cloud services (e.g., GCP) used to support production-intent analytics/modeling. Experience applying traditional NLP methods (tokenization, TF‑IDF, topic modeling, embeddings, clustering/classification) to unstructured text. Experience partnering with engineering teams to productionize models, including defining monitoring, drift response, and release gates. Master's degree in a quantitative field.