Principal Data Scientist

• 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.

• 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., * 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.

We offer a competitive benefits package that takes care of the whole you - both today and in the future:

• 401(k) with company match and Employee stock purchase plan
• Paid time for vacation, volunteering, and 28-day sabbatical after every 5 years of service for eligible positions
• Paid parental leave and family building benefits
• Tuition reimbursement
• Health, dental, and vision insurance

At Schwab, you're empowered to shape your future. We champion your growth through meaningful work, continuous learning, and a culture of trust and collaboration-so you can build the skills to make a lasting impact. Our Hybrid Work and Flexibility approach balances our ongoing commitment to workplace flexibility, serving our clients, and our strong belief in the value of being together in person on a regular basis.