Staff Data Scientist

Your Opportunity

At Schwab, you will build a rewarding career while making a difference in the lives of our millions of clients. Here, innovative thinking meets creative problem solving as we work together to challenge the status quo.You’llbe part of a collaborative, technology-forward environment that values curiosity, continuous learning, and thoughtful problem-solving.Schwab Data is the centralized organization that manages and enables the use of data as a strategic asset across Schwab, supporting enterprise analytics, platforms, and data-driven decision-making.Joining Schwab means joining a company committed to transforming the financial industry and putting clients at the center of everything we do.

Schwab’s AI & Data Science organizationis acentralizedhub for delivering innovative production ready AI and machine learning solutions that drive measurable business outcomes across the firm. The team partners with Schwab business units toidentifyhigh impactuse cases, pilot innovative analytical solutions, and transition successful models into enterprise level 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.

As a Staff 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 scalechallenges.You’llbridge advanced research and robust engineering, owning theend‑to‑endlifecycle ofhigh‑impactmodels.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 toidentifyand translate advanced analytical techniques, applications, and strategies into practical production ready solutions.

WhatYou’llDo

• Get hands-on with big dataas you analyze, interpret, extract insights, and produce innovative AI solutions that enable advanced decisioningtoleveragethe latest algorithms,state-of-the-arttechniques, and tools.

• Design and buildend-to-endmachine learning systemsby 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 executionby partnering with business stakeholders to converthigh-levelbusinessobjectivesinto clear, actionable data science and AI solutions that address critical business and technology challenges.

• Set and elevate engineering standards for data sciencebyestablishingbest 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 areasby leading complex initiatives involving advanced machine learning, recommender systems,real-timeandlow‑latencyinference, or other evolving technologies that require deep technicalexpertiseand comfort with ambiguity.

What you have

Required Qualifications

• 8+ years of experience in data science and machine learning.

• Advanced degree (Master’sor PhD) in a quantitative field such as computer engineering, statistics, mathematics, physics, chemistry, or related discipline.

• 6+ years ofhands-onexperience using Python and SQL to developproduction‑grade, modular, and optimized code.

• Proven ability to convert business requirements into technical end-to-end machine learning solutions delivered againstroadmapmilestonesformultiplelines of business.

• Proven experience developing supervised and unsupervised machine learning solutions, with deliverysupported by documented evaluation metrics, performance tracking, and value measurement.

• Experience in applying natural language processing techniques to unstructured data with deliveryto production.

• Practical experience designing LLM solutions (such asretrieval‑augmentedgeneration, agent workflows, orfine‑tuning), deployed for internal use.

• Strong software engineering fundamentals, including version control, CI/CD, andMLOpspracticesforproduction deployments.

Preferred Qualifications

• Strong background in statistics, forecasting, or causal inference.

• Hands-onexperience architecting machine learning solutions within cloud ecosystems(GCP, AWS, Azure)

• Experience building,maintaining, andoptimizingdata pipelines that support machine learning workflows.

• ProvenexpertiseinMLOpsand productionmodelmonitoring.

• 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 withdemonstratedability 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 strongtrack recordof performance.