AI & Data Scientist

Charles Schwab Inc.•Southlake, TX

1d•$110,000 - $155,000•Onsite

About The Position

Schwab’s AI & Data Science organization is the centralized hub for delivering responsible, production-grade AI and machine learning solutions that drive measurable business outcomes across the firm. The team partners with business units to identify high-impact use cases, pilot analytical solutions, and transition successful models into compliant, resilient production systems. Its mission is to accelerate AI adoption as a strategic product capability by ensuring models are scalable, reusable, governable, and continuously delivering value in a highly regulated environment. As a Senior AI & Data Scientist, you will drive the design, development, and implementation of innovative AI and machine learning solutions for complex, enterprise-scale challenges. You will bridge advanced research and robust engineering, owning the end-to-end lifecycle of high-impact models. You will collaborate with business sponsors, development teams, and engineering partners to translate advanced analytical techniques and strategies into practical, production-ready solutions. The role is intended to be performed on site in the specified location(s), reflecting Schwab’s emphasis on in-office collaboration.

Requirements

Experience in data science and machine learning.
Master’s or PhD in a quantitative field such as computer engineering, statistics, mathematics, physics, chemistry, or a related discipline.
Hands-on experience using Python and SQL to develop production-grade, modular, optimized code.
Ability to convert business requirements into end-to-end machine learning solutions delivered against roadmap milestones for two or more lines of business.
Experience developing supervised and unsupervised machine learning solutions, with at least three distinct models supported by documented evaluation metrics, performance tracking, and value measurement.
Experience applying natural language processing techniques to unstructured data, including at least one solution delivered to production.
Practical experience designing LLM solutions, such as retrieval-augmented generation, agent workflows, or fine-tuning, including at least one LLM system deployed for internal use.
Strong software engineering fundamentals, including version control, CI/CD, and MLOps practices, demonstrated through at least three production deployments.

Nice To Haves

Experience working in financial services or another highly regulated industry.
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.
Demonstrated commitment to mentorship, including coaching senior data scientists or engineers and improving team capability through feedback and code quality.
Outstanding verbal and written communication skills, with the ability to communicate effectively at all organizational levels.
Self-starter with strong organizational skills, attention to detail, and a desire to continually reevaluate existing products and processes.
Comfort in a dynamic, fast-moving environment, with a positive attitude, solid work ethic, and strong performance track record.

Responsibilities

Analyze and interpret big data, extract insights, and develop innovative AI solutions for advanced decision-making using current algorithms, techniques, and tools.
Design and build end-to-end machine learning systems, including scalable, reliable, and maintainable architectures for data ingestion, feature generation, model training, evaluation, deployment, monitoring, and production value measurement.
Partner with business stakeholders to translate business strategy and objectives into actionable data science and AI solutions for business and technology challenges.
Establish and elevate data science engineering standards, including modular code design, testing, version control, and production readiness.
Lead complex initiatives involving advanced machine learning, recommender systems, real-time and low-latency inference, or other evolving technologies.