Applied AI Lead Data Scientist - Vice President

JPMorganChase•Jersey City, NJ

About The Position

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 Technical Excellence : Portfolio of GenAI projects showcasing hands-on expertise in RAG systems, agentic AI, or LLM-powered applications with measurable business impact Delivery Excellence : Consistent track record of delivering complex projects on time with high quality, navigating ambiguity through structured approaches Business Impact Orientation : Clear examples of GenAI/AI/ML solutions that drove measurable business value with articulated linkage between technical solutions and outcomes Proactive Leadership : Evidence of independently driving initiatives forward, anticipating obstacles, and maintaining momentum without constant direction 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.

Requirements

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

Nice To Haves

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
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
Portfolio of GenAI projects showcasing hands-on expertise in RAG systems, agentic AI, or LLM-powered applications with measurable business impact
Consistent track record of delivering complex projects on time with high quality, navigating ambiguity through structured approaches
Clear examples of GenAI/AI/ML solutions that drove measurable business value with articulated linkage between technical solutions and outcomes
Evidence of independently driving initiatives forward, anticipating obstacles, and maintaining momentum without constant direction
Experience applying knowledge graphs, semantic layers, or graph-based reasoning to enhance GenAI solutions

Responsibilities

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
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
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