Senior AI and Data Scientist

Charles Schwab Inc.•Austin, TX

6d•$150,000 - $185,000•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 Schwab business units to identify high‑impact use cases, pilot innovative analytical solutions, and transition successful models into compliant, resilient production systems. Our mission is to accelerate the adoption of AI as a strategic product capability—ensuring models are scalable, reusable, governable, and continuously delivering value in a highly regulated environment. As a Senior AI & Data Scientist, you will play an essential part in advancing Schwab’s capabilities by driving the design, development, and implementation of innovative AI and machine learning solutions that address complex, enterprise‑scale challenges. You’ll bridge advanced research and robust engineering, owning the end‑to‑end lifecycle of high‑impact models. Successful candidates will work collaboratively across the organization with our business sponsors, development teams, and engineering partners. We are seeking a subject matter expert in all things AI, primed to identify and translate advanced analytical techniques, applications, and strategies into practical production ready solutions.

Requirements

8+ years of experience in data science and machine learning.
Advanced degree (Master’s or PhD) in a quantitative field such as computer engineering, statistics, mathematics, physics, chemistry, or related discipline.
6+ years of hands‑on experience using Python and SQL to develop production‑grade, modular, and optimized code.
Proven ability to convert business requirements into technical end-to-end machine learning solutions delivered against roadmap milestones for two or more lines of business.
Proven experience developing supervised and unsupervised machine learning solutions, with delivery of three or more distinct models supported by documented evaluation metrics, performance tracking, and value measurement.
Experience in applying natural language processing techniques to unstructured data with 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 three or more production deployments.

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.
Outstanding verbal and written communication skills with demonstrated ability to communicate effectively with all levels of the organization.
Self-starter with strong organizational skills, attention to detail, and desire to continually reevaluate existing products and processes.
Comfort in a dynamic, fast-moving environment, with a positive attitude, solid work ethic, and strong track records of performance.

Responsibilities

Get hands-on with big data as you analyze, interpret, extract insights, and produce innovative AI solutions that enable advanced decisioning leveraging the latest algorithms, state-of-the-art techniques, and tools.
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, monitoring, and value measurement in production environments.
Translate business strategy into technical execution by partnering with business stakeholders to convert high‑level business objectives into clear, actionable data science and AI solutions 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.