Principal Data Scientist

Charles Schwab Inc.•Austin, TX

15d•$215,000 - $235,000•Onsite

About The Position

At Schwab, you have the opportunity to do meaningful work that helps clients take control of their financial futures. You’ll be part of a collaborative, technology-forward environment that values curiosity, continuous learning, and thoughtful problem-solving. Schwab Technology Services (STS) enables innovative and reliable technology products that power how clients manage their money, supporting Schwab’s commitment to expanding access to investing and financial planning. We believe in the importance of in-office collaboration and fully intend for the selected candidate for this role to work on site in the specified location(s). In this role, you will be part of the Schwab AI & Data Science organization operating as a high-leverage, senior individual contributor, designing and helping deliver production-ready AI and machine learning systems that address complex, enterprise scale challenges. You will leverage deep expertise in advanced model building and deployment, acting as a hands-on technical authority who directly influences and supports engineering teams across the organization. A key focus will be providing architectural leadership and influencing technical strategy for real-time and low-latency systems to accelerate the firm’s AI and production ML maturity. This position offers the opportunity to shape how advanced data science capabilities are built, scaled, and operationalized across the organization.

Requirements

10+ years of experience in data science and machine learning, including 3+ years operating as a senior‑ or staff‑level individual contributor with significant technical ownership.
Advanced degree (Master’s or PhD) in a quantitative field such as computer engineering, statistics, mathematics, physics, chemistry, or a related discipline.
8+ years of hands‑on experience using Python and SQL to develop production‑grade, modular, and optimized code.
Demonstrated ability to architect and deliver end‑to‑end machine learning solutions, with evidence of at least two production systems supporting real‑time or low‑latency use cases.
Proven experience developing supervised and unsupervised machine learning solutions, with delivery of five or more distinct models or analytical systems supported by documented evaluation metrics and performance tracking.
Experience applying natural language processing techniques to unstructured data, supported by two or more delivered analyses or production components.
Practical experience designing large language model solutions (such as retrieval‑augmented generation, agent workflows, or fine‑tuning), including at least one end‑to‑end LLM system deployed for production or broad internal use.
Strong software engineering fundamentals, including version control, CI/CD, and MLOps practices, demonstrated through three or more production deployments.
Proven ability to convert business requirements into technical roadmaps, including ownership or co‑ownership of two or more roadmaps reviewed with senior stakeholders and delivered against defined milestones.

Nice To Haves

Experience working in financial services or other highly regulated industries.
Strong background in statistics, forecasting, or causal inference.
Hands‑on experience architecting machine learning solutions within cloud ecosystems.
Experience building, maintaining, and optimizing data pipelines that support machine learning workflows.
Experience developing large‑scale recommender or personalization systems.
A demonstrated commitment to mentorship, including coaching senior data scientists or engineers and elevating team capability through feedback and code quality.

Responsibilities

Design and build end‑to‑end machine learning systems by defining scalable, reliable, and maintainable architectures that support data ingestion, feature generation, model training, evaluation, deployment, and monitoring in production environments.
Translate business strategy into technical execution by partnering with senior leaders to convert high‑level business objectives into clear, actionable data science and AI roadmaps that address critical business and technology challenges.
Set and elevate engineering standards for data science by establishing best practices that treat data science as a rigorous engineering discipline, including modular code design, testing, version control, and production readiness.
Advance technical capabilities in emerging areas by leading complex initiatives involving advanced machine learning, recommender systems, real‑time and low‑latency inference, or other evolving technologies that require deep technical expertise and comfort with ambiguity.