Join the Global Services Insights & Analytics team and help transform data into actionable insights. You will collaborate with senior leaders and cross-functional partners to improve operational performance, efficiency, service, and controls. This role offers the opportunity to lead high-impact generative artificial intelligence solutions across financial services use cases while helping deliver reliable, scalable, and governed capabilities.
As a Vice President, Data Scientist Lead in the Global Services Insights & Analytics team, you will lead data-driven initiatives that enhance planning, efficiency, service, and controls within Commercial Banking. You will design and deliver large language model-powered solutions across high-impact use cases, including content extraction, enterprise search and question answering, reasoning, summarization, and recommendations. You will partner closely with engineering and product teams to deploy reliable, scalable, and governed generative artificial intelligence capabilities using Amazon Bedrock and Cortex platforms. Your work will emphasize evaluation, guardrails, and production-grade machine learning operations.
Job Responsibilities
- Develop and deliver generative artificial intelligence and large language model solutions for content extraction, semantic search, question answering, summarization, reasoning, and recommendation use cases
- Design, deploy, and manage prompt-based and retrieval-augmented generation systems, including orchestration patterns and agentic workflows such as tool use, structured outputs, and multi-step reasoning
- Build evaluation and testing frameworks to measure accuracy, faithfulness, robustness, latency, and cost, including red-teaming and safety checks where applicable
- Leverage Amazon Bedrock to prototype and productionize large language model applications, including model selection, prompt templates, routing, and deployment patterns
- Work hands-on with Cortex, including Cortex Analyst, to enable governed analytics experiences and generative artificial intelligence-assisted workflows
- Apply hands-on experience in environments such as Amazon Bedrock, Amazon SageMaker, or Databricks
- Collaborate with engineering teams to deliver scalable services, including application programming interfaces, batch jobs, and pipelines, while ensuring strong software engineering discipline and operational readiness
- Build and maintain data pipelines for structured and unstructured data, enabling retrieval, indexing, and preprocessing for large language model applications
- Conduct applied research by studying scientific articles and current techniques in prompting, fine-tuning, evaluation, and agent design, then translating them into practical improvements