Data Scientist Lead [multiple Positions Available]

JPMorgan Chase JPMorgan Chase · Banking · Chicago, IL +1 · Corporate Sector

This role focuses on driving analytics strategy for a commercial bank lending portfolio, with a specific emphasis on incorporating Generative AI and advanced analytics for credit risk monitoring. The Data Scientist Lead will build AI/ML tools, including prompt engineering and LLM training for text categorization, and manage end-to-end analytics projects. The role involves mentoring a team and ensuring alignment with business and regulatory objectives.

What you'd actually do

  1. Drive and execute a comprehensive analytics strategy for a commercial bank lending portfolio, aligning data science initiatives with business objectives to enable effective portfolio monitoring and support growth initiatives.
  2. Drive innovation and develop next generation data science capabilities through Gen AI and advanced analytics tools that deliver on enhanced portfolio surveillance on middle market companies and commercial real estate investors.
  3. Serve as a functional manager and subject matter expert on credit analytics processes and data products, ensuring alignment with business objectives.
  4. Collaborate with senior stakeholders to assess management needs on portfolio monitoring and insight analysis.
  5. Build requirements and shape analytics that drive risk strategy.

Skills

Required

  • building statistical, time-series or multivariate regression credit-risk models
  • client behavior classification and scoring
  • Correlating data patterns to intrinsic or external drivers
  • Statistical modeling using SAS, R, or Python
  • Delivering analytical services and advisory consulting
  • Managing data projects end-to-end including working with stakeholders on capturing requirements and supporting product user adoption
  • presenting technical solutions and results to executive-level audiences with compelling data visuals using PowerPoint, Tableau, or Qlik
  • Building and maintaining data workflow and process automation solutions using SAS or Alteryx on local or server instances
  • Querying large transactional and reference data using SQL on Oracle, Teradata, or AWS Redshift
  • incorporating Generative AI capabilities into credit risk monitoring frameworks by building AI and ML analytical tools with prompt engineering, accuracy assessment techniques, and LLM training for text categorization
  • Processing datasets through API calling for text categorization with AI and ML
  • Associating client financial metrics with industry-specific economic drivers
  • authoring technical specifications to data architecture and technology deployment teams
  • Authoring whitepapers to document approach and results
  • Meeting audit and regulatory requirements for analytical or modeling solutions
  • Implementing production solutions using Agile SDLC
  • Atlassian collaboration tools including Jira or Confluence

What the JD emphasized

  • credit risk monitoring frameworks
  • Generative AI capabilities
  • building AI and ML analytical tools
  • prompt engineering
  • accuracy assessment techniques
  • LLM training for text categorization
  • API calling for text categorization
  • Meeting audit and regulatory requirements

Other signals

  • Generative AI capabilities into credit risk monitoring frameworks
  • building AI and ML analytical tools with prompt engineering, accuracy assessment techniques, and LLM training for text categorization
  • Processing datasets through API calling for text categorization with AI and ML