Vice President - Data Scientist Lead (LLM/GenAI)

Join our Global Services Insights & Analytics Team and be at the forefront of transforming data into actionable insights. This role offers a unique opportunity to collaborate with senior executives and make a significant impact on operational performance and efficiency.

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 lead the design and delivery of LLM-powered solutions across high-impact use cases in financial services, including content extraction, enterprise search & Q&A, reasoning, summarization, and recommendations. You will partner closely with engineering and product teams to deploy reliable, scalable, and governed GenAI capabilities, leveraging Amazon Bedrock and Cortex platforms, with strong emphasis on evaluation, guardrails, and production-grade MLOps.

Job Responsibilities

• Develop and deliver GenAI/LLM solutions for problems such as content extraction, semantic search, question answering, summarization, reasoning, and recommendation.
• Design, deploy, and manage prompt-based and RAG-based systems, including orchestration patterns and agentic workflows (tool use, structured outputs, multi-step reasoning).
• Build comprehensive evaluation and testing frameworks (offline + online) to measure accuracy, faithfulness, robustness, latency, and cost; implement red-teaming and safety checks where applicable.
• Leverage Amazon Bedrock to prototype and productionize LLM applications, including model selection, prompt templates, routing, and deployment patterns.
• Work hands-on with Cortex (e.g., Cortex Analyst) to enable governed analytics experiences and GenAI-assisted workflows.
• Hands-on experience working in environments such as AWS Bedrock, Amazon SageMaker or Databricks
• Collaborate with engineering teams to deliver scalable services (APIs, batch jobs, pipelines), ensuring strong software engineering discipline and operational readiness.
• Build and maintain data pipelines for structured and unstructured data, enabling retrieval, indexing, and preprocessing for LLM applications.
• Conduct applied research by studying scientific articles and state-of-the-art techniques (prompting, fine-tuning, evaluation, agent design) and translating them into practical improvements.
• Communicate clearly with technical and non-technical stakeholders, translating business needs into measurable problem statements, solution designs, and success metrics.
• Mentor and lead junior data scientists, influence standards, and drive adoption of responsible AI practices.

• Advanced degree (Masters preferred) in Data Science, Computer Science, Machine Learning, Statistics, or related quantitative field (or equivalent practical experience).
• 5 -7 years of relevant applied experience building ML/NLP solutions, including production deployment in a fast-paced environment.
• Proven NLP + LLM experience, including prompt engineering, RAG, and evaluation methodologies.
• Hands-on experience with Amazon Bedrock (or equivalent managed LLM platform) for building and deploying GenAI solutions.
• Experience with Cortex (e.g., Cortex Analyst and related workflows) in an enterprise setting.
• Strong Python skills; familiarity with ML/DL frameworks such as PyTorch or TensorFlow, and standard ML tooling (pandas, NumPy, scikit-learn).
• Experience building APIs and integrating LLM/NLP solutions into applications and services.
• Data pipeline experience for structured/unstructured data processing; strong understanding of embeddings, vector search, indexing, and retrieval patterns.
• Solid software engineering practices: Git/version control, code quality, testing, and CI/CD fundamentals.
• Excellent communication and stakeholder management skills; ability to present tradeoffs, risks, and results concisely.
• Strong analytical skills and working knowledge of financial services / markets / asset management concepts.

• Deep understanding of Large Language Model (LLM) techniques, including Agents, Planning, Reasoning, and related methods.
• MLOps experience: experiment tracking, model registry, monitoring, drift/performance tracking, incident management, rollback.
• Experience with cloud deployment patterns (AWS preferred) and production runtime environments (containers/orchestration).