Principal Post-Silicon Validation Engineer

🏢 Summary: Staff Data Scientist role focused on designing, deploying, and operating enterprise-scale AI and machine-learning solutions. The position owns end-to-end model lifecycles, translating business goals into scalable production systems and advancing capabilities in areas such as NLP, LLMs, recommender systems, and low-latency inference. 🗂️ Requirements: 8+ years of data science and machine learning experience, Master’s or PhD in a quantitative field, 6+ years of hands-on Python and SQL experience, Production-grade modular and optimized code development, End-to-end machine learning solution delivery from business requirements, Supervised and unsupervised machine learning experience, Documented model evaluation, performance tracking, and value measurement, NLP solutions for unstructured data deployed to production, Production deployment of LLM solutions, Version control, CI/CD, and MLOps experience 📃 Skills: Python, SQL, NLP, LLMs, MLOps, CICD 🏢 Description: Your opportunity Schwab Data is the centralized organization that manages and enables the use of data as a strategic asset, supporting enterprise analytics, platforms, and data-driven decision-making. Schwab’s AI & Data Science organization delivers production-ready AI and machine learning solutions that drive measurable business outcomes. The team partners with business units to identify high-impact use cases, pilot analytical solutions, and transition successful models into enterprise-level production systems. Its mission is to accelerate adoption of AI as a strategic product capability, ensuring models are scalable, reusable, governable, and continuously deliver value. Description As a Staff Data Scientist, you will drive the design, development, and implementation of 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 and collaborating with business sponsors, development teams, and engineering partners. This role requires a subject-matter expert who can translate advanced analytical techniques, applications, and strategies into practical, production-ready solutions. What You’ll Do • Analyze and interpret big data, extract insights, and produce innovative AI solutions that enable advanced decision-making using current algorithms, techniques, and tools. • Design and build end-to-end machine learning systems, including scalable and maintainable architectures for data ingestion, feature generation, model training, evaluation, deployment, monitoring, and production value measurement. • Partner with business stakeholders to translate high-level objectives into actionable data science and AI solutions for critical business and technology challenges. • Establish engineering best practices for data science, 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, and other emerging technologies. Required Qualifications • 8+ years of experience in data science and machine learning. • Advanced degree (Master’s or PhD) in computer engineering, statistics, mathematics, physics, chemistry, or a related quantitative discipline. • 6+ years of hands-on experience using Python and SQL to develop production-grade, modular, optimized code. • Proven ability to convert business requirements into end-to-end machine learning solutions delivered against roadmap milestones for multiple lines of business. • Proven experience developing supervised and unsupervised machine learning solutions, supported by documented evaluation metrics, performance tracking, and value measurement. • Experience applying natural language processing techniques to unstructured data and deploying solutions 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 in cloud ecosystems, including GCP, AWS, or Azure. • Experience building, maintaining, and optimizing data pipelines that support machine learning workflows. • Expertise in MLOps and production model monitoring. • Demonstrated mentorship experience, including coaching senior data scientists or engineers and improving team capability through feedback and code quality. • Strong verbal and written communication skills across all organizational levels. • Strong organizational skills, attention to detail, and a desire to continually reevaluate products and processes. • Ability to succeed in a dynamic, fast-moving environment with a positive attitude, solid work ethic, and strong performance record. Compensation In addition to the salary range, this role is eligible for bonus or incentive opportunities. Benefits The hybrid work and flexibility approach balances workplace flexibility, client service, and regular in-person collaboration. • 401(k) with company match and employee stock purchase plan. • Paid vacation, volunteering time, and a 28-day sabbatical after every five years of service for eligible positions. • Paid parental leave and family-building benefits. • Tuition reimbursement. • Health, dental, and vision insurance.