Manager, Data Scientist
Confirmed on the employer's own hiring board on Nov 11, 2026. First seen by Alion on Nov 11, 2026. Charles Schwab scores A on the Alion truth index.
Your Opportunity
At Schwab, you’re empowered to make an impact on your career. Here, innovative thought meets creative problem solving, helping us “challenge the status quo” and transform the finance industry together. We believe in the importance of in-office collaboration and fully intend for the selected candidate for this role to work on site in the specified location.
The Behavioral AI/ML Analytics (BAMA) team is looking for a dynamic, self-motivated Manager, Data Science. BAMA leverages predictive modeling, advanced analytics, machine learning, and emerging AI technologies to deliver innovative solutions that enhance Schwab's ability to fight financial crime. Supporting Fraud Prevention and Anti-Money Laundering (AML) programs within Financial Risk Management (FRM), the team develops predictive models, AI-powered capabilities, and advanced analytical solutions that solve complex business problems and deliver measurable value. With a broad range of business challenges to address, BAMA provides numerous opportunities to drive meaningful impact through both traditional machine learning and next-generation AI technologies.
What you’ll do:As a Manager, Data Science, you will play a key role in understanding FRM business needs and delivering innovative, high-impact analytical solutions. You will collaborate with business, technology, and governance partners to develop and deploy advanced analytics, machine learning, and Generative AI solutions that support critical fraud and AML initiatives.
Key responsibilities include:
Develop, test, and implement predictive modeling, machine learning, and GenAI-powered solutions to enhance fraud detection, AML investigations, risk analytics, and operational efficiency.
Partner with business, technology, and governance teams to translate business needs into practical use cases for Large Language Models (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 while supporting appropriate model governance, risk controls, and regulatory compliance.
Leverage 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, supporting informed business decisions and measurable outcomes.
What you have
The Manager, Data Scientist brings a strong quantitative foundation, business acumen, and passion for solving complex problems. The ideal candidate is an innovative, collaborative professional with expertise in data science, machine learning, and emerging AI technologies.
Qualifications:
Demonstrated ability to independently and collaboratively deliver end-to-end data science solutions that drive business value.
Experience applying data science and machine learning within fraud, AML, payments, financial services, or related domains.
Strong communication skills, with the ability to translate complex analytical and AI concepts into clear business insights.
Curiosity and enthusiasm for learning and 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 technologies, including Large Language Models (LLMs), Retrieval-Augmented Generation (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.
What will help set you apart:
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