Staff Data Scientist

About the role

The Schwab Data organization is responsible for managing and enabling the use of data as a strategic asset across Schwab. This includes supporting enterprise analytics, platforms, and data-driven decision-making. The AI & Data Science organization focuses on delivering innovative production-ready AI and machine learning solutions that drive measurable business outcomes.

What You’ll Do

• Get hands-on with big data as you analyze, interpret, extract insights, and produce innovative AI solutions that enable advanced decisioning using 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.

Required Qualifications

• 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 multiple lines of business.
• Proven experience developing supervised and unsupervised machine learning solutions, with delivery supported by documented evaluation metrics, performance tracking, and value measurement.
• Experience in applying natural language processing techniques to unstructured data with delivery to production.
• Practical experience designing LLM solutions (such as retrieval-augmented generation, agent workflows, or fine-tuning), deployed for internal use.
• Strong software engineering fundamentals, including version control, CI/CD, and MLOps practices for production deployments.

Preferred Qualifications

• Strong background in statistics, forecasting, or causal inference.
• Hands-on experience architecting machine learning solutions within cloud ecosystems (GCP, AWS, Azure).
• Experience building, maintaining, and optimizing data pipelines that support machine learning workflows.
• Proven expertise in MLOps and production model monitoring.
• 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 record of performance.