Investment Bank Internal Audit - Vice President - Data Scientist

JPMorgan Chase JPMorgan Chase · Banking · Plano, TX +1 · Corporate Sector

Data Scientist role focused on refining and prototyping advanced analytics and machine learning ideas within Internal Audit at JPMorgan Chase. The role involves partnering with stakeholders, leading feasibility assessments, developing proof-of-concept prototypes, and ensuring effective hand-off to product owners for downstream delivery. Requires strong ML/DL/NLP knowledge and leadership skills.

What you'd actually do

  1. Evaluate and develop new analytics ideas, ensuring alignment with audit priorities and organizational goal
  2. Facilitate stakeholder sessions to refine problem statements and confirm a shared understanding of the need
  3. Define expected benefits and gather empirical evidence to support proposals and prioritization
  4. Present findings and recommendations to decision-makers to support clear Go/No-Go outcomes
  5. Lead feasibility work (environment setup, data acquisition, prototyping, and results review) and iterate based on learnings

Skills

Required

  • Advanced knowledge of machine learning methods
  • deep learning frameworks
  • natural language processing techniques
  • Strong analytical and problem-solving skills
  • Excellent written and verbal communication skills
  • Experience managing multiple initiatives concurrently
  • Hands-on technical proficiency in data analysis and visualization tools
  • Strong documentation discipline

Nice to have

  • Experience building proof-of-concept prototypes and transitioning them to delivery teams through structured hand-offs
  • Familiarity with analytics use in audit, risk, compliance, or control testing environments
  • Experience facilitating working sessions with senior leaders and cross-functional teams
  • Proficiency with common project management tools used to track and deliver concurrent initiatives

What the JD emphasized

  • development-ready prototypes
  • development-ready artifacts

Other signals

  • prototyping advanced analytics ideas
  • refinement and prototyping of advanced analytics
  • guide concepts from early problem framing through feasibility assessment, proof-of-concept development
  • lead feasibility work (environment setup, data acquisition, prototyping, and results review)
  • produce clear documentation and ensure effective hand-off of artifacts to the product owner for downstream delivery