Credit Risk Innovation VP - Corporate & Tcio

JPMorgan Chase JPMorgan Chase · Banking · Mumbai, Maharashtra, India · Corporate Sector

This role focuses on developing and launching innovative credit risk analytics products within a banking context, leveraging AI/ML, LLMs, and automation. The VP will lead the end-to-end product lifecycle, architect platforms, integrate advanced risk models, and collaborate with cross-functional teams. Key responsibilities include product ideation, user story mapping, MVP definition, launch, and iteration, with a strong emphasis on data-driven, scalable, and regulatory-compliant solutions. The role involves hands-on experience with Python/PySpark, cloud technologies, and agile methodologies, and a solid understanding of credit risk and banking products.

What you'd actually do

  1. Lead the end-to-end lifecycle of credit risk analytics products—from ideation, user story mapping, and MVP definition to launch and iteration.
  2. Architect and develop platforms using Python/PySpark, awareness of cloud technologies (AWS, Azure), and modern front-end frameworks (React, Angular) to deliver robust credit risk tools.
  3. Integrate advanced risk models and analytics into banking products and portfolios, ensuring solutions are data-driven, scalable, and regulatory-compliant.
  4. Leverage AI/ML, LLMs, and automation to enhance credit risk review, underwriting, and monitoring processes.
  5. Develop product proposals, manage backlogs, prioritize features, and communicate progress to stakeholders.

Skills

Required

  • 8-10 years’ experience in product development, data science, or credit risk analytics with Bachelor’s degree in a quantitative or technical field (CS, engineering, math, etc.).
  • Experience in product management or product development, ideally in risk analytics or banking technology.
  • Proficiency in Python/PySpark, cloud platforms, data visualization (Tableau), and front-end/back-end development.
  • Solid understanding of credit risk, banking products, portfolio analytics, and regulatory requirements.
  • Hands-on experience with agile frameworks, sprint planning, and backlog management.
  • Ability to break down complex business challenges and deliver practical, scalable solutions.
  • Skilled at translating technical concepts for business stakeholders and driving cross-team collaboration.

Nice to have

  • Practical experience with Generative AI, large language models (LLMs), and automation in financial services.
  • Hands-on expertise with NLP frameworks (e.g., Hugging Face Transformers, spaCy, NLTK) and prompt engineering for generative AI.
  • Knowledge of Retrieval-Augmented Generation (RAG) architectures and enterprise AI applications.
  • Experience implementing, fine-tuning, and integrating AI/ML models with cloud platforms and cloud-native services.
  • Solid understanding of MLOps practices for deploying, monitoring, and maintaining AI solutions.
  • Strong UI/UX and dashboarding skills.
  • Proficient with agile tools (JIRA, Confluence) and project management methodologies.

What the JD emphasized

  • regulatory-compliant
  • regulatory requirements

Other signals

  • Leverage AI/ML, LLMs, and automation to enhance credit risk review, underwriting, and monitoring processes.
  • Practical experience with Generative AI, large language models (LLMs), and automation in financial services.
  • Hands-on expertise with NLP frameworks (e.g., Hugging Face Transformers, spaCy, NLTK) and prompt engineering for generative AI.
  • Knowledge of Retrieval-Augmented Generation (RAG) architectures and enterprise AI applications.
  • Experience implementing, fine-tuning, and integrating AI/ML models with cloud platforms and cloud-native services.