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 aVice President, Data Scientist Leadin 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 ofLLM-powered solutionsacross high-impact use cases in financial services, includingcontent extraction,enterprise search & Q&A,reasoning,summarization, andrecommendations. You will partner closely with engineering and product teams to deployreliable, scalable, and governedGenAI capabilities, leveragingAmazon BedrockandCortexplatforms, with strong emphasis onevaluation, guardrails, and production-grade MLOps.
Job Responsibilities
- Develop and deliver GenAI/LLM solutionsfor 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 Bedrockto 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 teamsto deliver scalable services (APIs, batch jobs, pipelines), ensuring strong software engineering discipline and operational readiness.
- Build and maintain data pipelinesfor structured and unstructured data, enabling retrieval, indexing, and preprocessing for LLM applications.
- Conduct applied researchby 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 leadjunior data scientists, influence standards, and drive adoption of responsible AI practices.
Required Qualifications, Skills & Capabilities
- Advanced degree(Masters preferred) in Data Science, Computer Science, Machine Learning, Statistics, or related quantitative field (or equivalent practical experience).
- 5 -7 yearsof relevant applied experience building ML/NLP solutions, includingproduction deploymentin 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 Pythonskills familiarity with ML/DL frameworks such asPyTorchorTensorFlow, and standard ML tooling (pandas, NumPy, scikit-learn).
- Experience buildingAPIsand integrating LLM/NLP solutions into applications and services.
- Data pipeline experiencefor structured/unstructured data processing strong understanding of embeddings, vector search, indexing, and retrieval patterns.
- Solidsoftware engineering practices: Git/version control, code quality, testing, and CI/CD fundamentals.
- Excellent communication and stakeholder managementskills ability to present tradeoffs, risks, and results concisely.
- Strong analytical skills and working knowledge offinancial services / markets / asset managementconcepts.
Preferred Qualifications
- 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 withcloud deployment patterns(AWS preferred) and production runtime environments (containers/orchestration).
