Applied AI Lead Data Scientist - Vice President

Position Overview

Join our Applied AI/ML team as an AI/ML Solutions Lead (Vice President) driving high-impact GenAI initiatives across Consumer & Community Banking. This is a hands-on GenAI-focused data science position requiring a proven track record of delivering business-impactful projects from conception through scaled deployment. You will own the end-to-end lifecycle of GenAI use cases, serving as both technical leader and strategic partner while maintaining hands-on involvement in solution architecture, prototyping, and implementation.

Core Responsibilities

Strategic Delivery & Technical Leadership

• Own end-to-end delivery of GenAI and AI/ML use cases with ability to independently plan, anticipate complexities, and deliver high-quality outputs within agreed timelines
• Hands-on development and prototyping of GenAI solutions, including building POCs, architecting scalable solutions, and writing production-quality code
• Design and implement GenAI solutions leveraging LLMs, agentic AI systems, and RAG architectures
• Build end-to-end data pipelines using Python and modern platforms (Snowflake, Databricks), implementing ETL/ELT processes for model development
• Proactively identify and communicate risks, delays, and mitigation options with structured progress updates

Thought Leadership & Stakeholder Management

• Develop and evangelize strategic vision for GenAI solutions, generating clarity in uncertain environments through deep customer engagement
• Proactively engage with stakeholders to confirm expectations, surface uncertainties early, and maintain alignment throughout project lifecycles
• Own communication cadence with executive stakeholders, providing forward-looking updates without repeated prompting
• Demonstrate analytical rigor and storytelling, clearly linking findings, implications, and recommended actions

Execution & Change Management

• Independently develop comprehensive project plans, identify stakeholders, define success metrics, and drive execution with minimal direction
• Develop and implement best practices for integrating GenAI solutions as scalable, enterprise-grade capabilities
• Collaborate across cross-functional teams to identify strategic partners and foster collaborative environments

Required Qualifications

Critical Technical Skills

• Advanced Python programming with ability to write production-quality, maintainable code for data science and AI/ML applications
• Hands-on GenAI experience:
  • Large Language Models (e.g. GPT, Claude, Llama) including prompt engineering and fine-tuning
  • Agentic AI frameworks (e.g. LangChain, LlamaIndex, AutoGen, CrewAI)
  • RAG architectures including vector databases (Pinecone, Weaviate, ChromaDB, FAISS), embedding models, and retrieval optimization
• Modern data platforms:
  • Snowflake for data warehousing and analytics
  • Databricks for distributed computing and ML workflows
  • ETL/ELT processes and data pipeline orchestration
  • Cloud platforms (AWS, Azure, GCP) and their AI/ML services
• Core data science packages: pandas, numpy, scikit-learn, PyTorch/TensorFlow
• MLOps practices: model versioning, experiment tracking (MLflow), deployment pipelines, version control (Git), containerization (Docker)

Critical Business & Leadership Capabilities

• Proven track record of delivering business-impactful GenAI/AI/ML projects in large enterprise environments from ambiguous requirements to scaled production
• End-to-end project planning and execution with ability to independently manage timelines, resources, risks, and stakeholder expectations across concurrent initiatives
• Operating in uncertainty: demonstrated capability to generate clarity through structured problem-solving, customer engagement, and iterative refinement
• Excellent communication and stakeholder management with proven ability to influence senior leaders and drive alignment across diverse teams
• Self-directed work style with ability to anticipate complexities, proactively identify risks, and maintain disciplined progress with limited supervision
• Strong analytical and problem-solving skills with demonstrated rigor in structuring problems and developing actionable recommendations

Education

• Bachelor's degree in Computer Science, Engineering, Data Science, Mathematics, Statistics, or equivalent practical experience

Preferred Qualifications

Advanced Technical Skills

• Advanced degree (Master's or PhD) in Computer Science, Data Science, Machine Learning, or related quantitative field
• Semantic technologies and graph databases:
  • Property graphs and graph databases (Neo4j, TigerGraph, Amazon Neptune) with Cypher query language
  • RDF graphs and semantic web technologies (RDF, OWL, SPARQL)
  • Knowledge graph construction, ontology design, and taxonomy development
  • Integration of graph-based approaches with GenAI solutions (GraphRAG, knowledge-enhanced LLMs)
• Advanced RAG techniques: hybrid search, re-ranking, query decomposition, multi-hop reasoning
• Experience with model evaluation frameworks and responsible AI practices

Business & Leadership

• Experience with change management principles and organizational adoption of AI/ML capabilities
• Familiarity with Agile methodologies and modern product management frameworks
• Track record of thought leadership through publications, presentations, or recognized contributions
• Experience in financial services or highly regulated industries
• Demonstrated ability to mentor team members and set high standards for execution quality

What Sets Successful Candidates Apart

1. Technical Excellence: Portfolio of GenAI projects showcasing hands-on expertise in RAG systems, agentic AI, or LLM-powered applications with measurable business impact
2. Delivery Excellence: Consistent track record of delivering complex projects on time with high quality, navigating ambiguity through structured approaches
3. Business Impact Orientation: Clear examples of GenAI/AI/ML solutions that drove measurable business value with articulated linkage between technical solutions and outcomes
4. Proactive Leadership: Evidence of independently driving initiatives forward, anticipating obstacles, and maintaining momentum without constant direction
5. Semantic & Graph Expertise (Plus): Experience applying knowledge graphs, semantic layers, or graph-based reasoning to enhance GenAI solutions

Technical Environment

Languages: Python, SQL | GenAI: SmartSDK, LangChain, LlamaIndex, OpenAI/Anthropic APIs | Data Platforms: Snowflake, Databricks, AWS/Azure/GCP | ML/DL: PyTorch, TensorFlow, scikit-learn | Vector DBs: Pinecone, Weaviate, ChromaDB, FAISS | Graph DBs: Neo4j, TigerGraph, Amazon Neptune | MLOps: MLflow, Docker, Kubernetes, Git

JPMorgan Chase is an equal opportunity employer committed to creating an inclusive environment for all employees.