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

• Bachelors degree in Computer Science Engineering Data Science Mathematics Statistics or equivalent practical experience

Preferred Qualifications

Advanced Technical Skills

• Advanced degree (Masters 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.