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. Please note: 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”

• Build and scale a supervision “model factory” by applying strong data science best practices across a portfolio of risk-category models: well-organized, version-controlled code; reproducible data pipelines; repeatable feature engineering; consistent evaluation harnesses; and standardized documentation/templates.

• Establish and maintain portfolio standards (dataset curation conventions, feature definitions, labeling guidance, evaluation conventions, documentation structure, and release readiness criteria) to enable consistent delivery at scale.

Lead architecture and delivery of deployable AI systems

• Serve as the embedded data scientist within the supervisory organization: lead analytical design and model development, while partnering closely with architects and engineers to enable repeatable, stable deployments into approved production pathways.

• Select appropriate approaches (classical ML, NLP, LLM/RAG, hybrid), justify tradeoffs, and establish baselines. Provide clear specifications (features, thresholds, output schemas, and monitoring requirements) that engineering teams can productionize reliably.

• Collaborate with engineering/platform partners to ensure model solutions meet operational constraints (latency, cost, throughput, maintainability) without compromising measurement integrity, auditability, or defensibility.

Evaluation, controls, and defensibility

• Design evaluation harnesses aligned to supervision outcomes: precision/recall by severity tier, false-negative containment strategies, threshold optimization, calibration, drift detection, and reviewer agreement versus human evaluations.

• Perform disciplined error analysis and remediation planning: slice-based performance (by product, channel, rep behaviors, client segments, doc types), root-cause analysis of false positives/false negatives, and concrete corrective actions (data, labels, features, thresholds, reviewer guidance).

• Implement evidence & auditability requirements in partnership with stakeholders and engineering teams: reason codes/attribution strategies, input lineage expectations, model/version traceability, reproducible runs, and output logging suitable for exam readiness.

• Build guardrails and safe-failure behaviors (conservative defaults, abstention/uncertainty handling, escalation logic, and human-in-the-loop triggers) to ensure supervisors remain firmly in the loop.

Documentation and model risk artifacts

• Own model evidence packages (model card/whitepaper): training data description, labeling methodology and quality assessment, evaluation results vs human baselines, known limitations, monitoring plan, change history, and release gates — in partnership with the Supervision PO/SMEs and aligned to model risk expectations.

Data readiness + access patterns

• Define required tables/fields, refresh expectations, data quality checks, and lineage requirements; partner with data teams to enable approved access patterns for model development, scoring, and monitoring

• Define scalable labeling/ground-truth strategies with SMEs (taxonomies, sampling plans, adjudication workflows, inter-rater reliability) to ensure