Manager, Data Scientist

Charles Schwab; San Francisco (CA); On-site; USD 100,000 - 140,000; Full time

Benefits offered by this job

401(k) with company match

Employee stock purchase plan

Vacation and volunteering time off

28-day sabbatical after 5 years

Paid parental leave and family forming

Tuition reimbursement

Health, dental, and vision insurance

Job summary

Charles Schwab’s Behavioral AI/ML Analytics (BAMA) team seeks a Manager, Data Scientist to deliver predictive modeling, GenAI, and investigator tools with governance. You will apply LLM/RAG and AI agent frameworks to fraud detection, AML investigations, risk analytics, and operational efficiency, collaborating across business, technology, and governance partners.

Role emphasizes governance, explainability, and regulatory compliance while leveraging Vertex AI, Gemini, OpenAI, and LangChain in a

Qualifications

• Demonstrated ability to independently and collaboratively deliver end-to-end data science solutions.
• Experience in fraud, AML, payments, financial services or related domains.
• Strong communication, translating analytics into business insight.
• Curiosity for new tools, technologies, and techniques.
• 3+ years of analytics, data science, ML or related field.
• Advanced SQL and Python proficiency.
• Experience with Generative AI, LLMs, RAG, prompt engineering, or AI agents.
• Understanding of governance, explainability and AI risk management.

Responsibilities

• Develop, test, and implement predictive modeling and GenAI-powered solutions for fraud detection and AML investigations.
• Partner with business, tech, and governance teams to turn needs into LLM/RAG use cases.
• Design AI-assisted investigator tools, knowledge retrieval, and decision-support with governance in mind.
• Use cloud AI platforms to improve analyst productivity and decision quality.
• Communicate analytical findings to technical and non-technical stakeholders.

Skills

End-to-end data science

Fraud & AML analytics

SQL

Python

GenAI & LLMs

RAG / retrieval augmented generation

Prompt engineering

AI governance & risk management

Communication skills

Curiosity / new tools adoption

Tools

Vertex AI

Gemini

OpenAI

LangChain

Job description

Charles Schwab’s Behavioral AI/ML Analytics (BAMA) team is building advanced data science capabilities to support Financial Risk Management, with a focus on Fraud Prevention and Anti-Money Laundering (AML). In this Manager, Data Scientist role, you will help deliver predictive modeling, machine learning, and Generative AI solutions, including LLM/RAG and investigator decision-support tools, with strong attention to governance and regulatory compliance.

Role overview

This position centers on developing and applying AI to fraud detection, AML investigations, risk analytics, and operational efficiency. You will work across business, technology, and governance partners to translate real-world needs into practical LLM and agentic AI use cases, while designing tools that support explainable, governed, and compliant decision-making.

Responsibilities

• Develop, test, and implement predictive modeling, machine learning, and GenAI-powered solutions to strengthen fraud detection, AML investigations, risk analytics, and operational efficiency.
• Partner with business, technology, and governance teams to convert business needs into use cases for LLMs, Retrieval Augmented Generation (RAG), intelligent automation, and agentic AI systems.
• Design, develop, and evaluate AI-assisted investigator tools, knowledge retrieval solutions, and decision-support capabilities, supporting model governance, risk controls, and regulatory compliance.
• Use cloud-based AI platforms and emerging technologies to improve analyst productivity, decision quality, and customer outcomes.
• Communicate analytical findings and AI-driven insights to technical and non-technical stakeholders to support informed decisions and measurable outcomes.

Requirements

• Demonstrated ability to independently and collaboratively deliver end-to-end data science solutions that drive business value.
• Experience in analytics and machine learning within fraud, AML, payments, financial services, or related domains.
• Strong communication skills, including the ability to translate complex analytical and AI concepts into clear business insights.
• Curiosity and enthusiasm for adopting new tools, technologies, and techniques.
• 3+ years of experience in analytics, data science, machine learning, or a related field.
• Advanced proficiency in SQL and Python.
• Experience with Generative AI, including LLMs, RAG, prompt engineering, or AI agent frameworks.
• Understanding of Responsible AI, model governance, explainability, and AI risk management principles.
• Experience with modern AI platforms and tools such as Vertex AI, Gemini, OpenAI, LangChain, or related technologies.

Technologies

• SQL, Python
• Large Language Models (LLMs), Retrieval Augmented Generation (RAG), prompt engineering, AI agent frameworks
• Vertex AI, Gemini, OpenAI, LangChain
• Generative AI, Google Cloud Platform (GCP)
• Intelligent automation, agentic AI systems

Location

• Southlake, TX
• Omaha, NE
• Orlando, FL
• San Francisco, CA (onsite)

Compensation

USD 100,000 - 140,000 per year

Benefits

• 401(k) with company match and Employee stock purchase plan
• Paid time for vacation and volunteering, plus a 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

Additional experience that can help

• Demonstrated experience developing and deploying GenAI applications within financial services, fraud, AML, risk management, or compliance domains.
• Experience with LLM evaluation, AI observability, prompt optimization, RAG architectures, knowledge retrieval systems, and agent-based workflows.
• Knowledge of cloud-native AI platforms, particularly Google Cloud Platform (GCP), Vertex AI, Gemini, and related AI services.
• Experience supporting cross-functional AI initiatives from ideation through production deployment and adoption.
• Familiarity with AI governance frameworks, model risk management, and emerging regulatory expectations for GenAI in highly regulated industries.
• Experience building scalable AI solutions that integrate predictive modeling, machine learning, and Generative AI capabilities into operational workflows.