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Applied AI & Data Product Scientist

JPMorgan Chase
Jersey City, USA
Full-time
Senior · 8+ years experience
Onsite
Discovered 1 weeks ago
awsdatabrickslangchainpower-bipythonsnowflake

Full Job Posting

Job Responsibilities

  • Lead research and rapid prototyping of emerging AI capabilities — including agentic workflows, multi-agent orchestration, retrieval-augmented generation, and advanced forecasting — converting validated proofs of concept into production-grade products on a defined timeline
  • Maintain a rolling 6–12 month innovation pipeline by identifying, evaluating, and experimenting with new models, frameworks, and techniques so the team always has a next release in development
  • Architect, build, and scale large language model agents and AI-driven solutions for Finance workflows, integrating with APIs, microservices, reporting tools, and cloud platforms
  • Design evaluation harnesses, guardrails, and human-in-the-loop review processes for every AI product from prototype through production
  • Translate model outputs into user-friendly insights, analytics, and decision-support surfaces for Finance leadership and front office partners
  • Audit and remediate data quality and lineage gaps across reporting and analytics products so all downstream consumers work from a single source of truth
  • Define and implement a data catalogue strategy by inventorying existing assets, consolidating duplicative pipeline logic, standardizing metric definitions, and establishing clear data contracts
  • Architect curated and semantic data layers that are consistent, trusted, and reusable across reporting, dashboards, analytics, and AI products
  • Establish data observability practices including monitoring, alerting, and lineage tracking so issues surface before they reach end users
  • Define and own a forward-looking AI and data roadmap aligned to Finance priorities and the firm's broader technology direction
  • Design experimentation and measurement frameworks to quantify the business impact of AI products and data quality improvements, and communicate roadmap, trade-offs, and results to senior leaders through clear narratives and data-driven recommendations

Required Qualifications, Capabilities, and Skills

  • 8 years of experience in data science, AI/ML, analytics, or product engineering, with hands-on delivery of production AI or ML applications
  • Hands-on experience with large language models including prompt engineering, retrieval-augmented generation, function and tool calling, embeddings, vector stores, and evaluation frameworks
  • Strong Python and SQL fluency with experience building and operating production data and inference pipelines end-to-end
  • Practical expertise with cloud data platforms such as AWS, Snowflake, or Databricks, including distributed processing and data modeling
  • Demonstrated ability to take AI or generative AI solutions from concept through prototype to scaled production
  • Experience diagnosing and fixing data quality, lineage, and governance problems in complex enterprise environments
  • Understanding of Finance functions such as financial planning and analysis, management reporting, forecasting, and key performance indicator frameworks within asset management, wealth management, or comparable financial services
  • Proficiency in applied statistics and experimentation design with the ability to measure business impact and communicate findings to non-technical audiences
  • Bachelor's degree in Computer Science, Engineering, Data Science, Mathematics, Statistics, Finance, or a related quantitative field

Preferred Qualifications, Capabilities, and Skills

  • Experience building multi-agent systems, autonomous workflows, or task planners using frameworks such as LangGraph, LangChain, or LlamaIndex
  • Hands-on experience building generative AI products with guardrails, evaluation harnesses, and human-in-the-loop review grounded in approved internal sources
  • Experience designing and governing semantic layers, metric stores, and data observability frameworks that support both business intelligence and AI consumption
  • Track record of designing and operationalizing experimentation programs including A/B testing and causal measurement to quantify product and data initiatives
  • Knowledge of model safety, bias mitigation, privacy techniques, and AI governance frameworks in financial services
  • Familiarity with modern business intelligence and visualization tools such as Tableau, Power BI, or ThoughtSpot
  • Prior experience in a Finance transformation or technology function supporting front office or investment management

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