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This listing doesn't post a salary. Most similar roles pay
$161,250–$225,312.
Based on 240 similar postings.
Employer
About JPMorgan Chase
JPMorgan Chase & Co. is a global financial services firm and one of the largest banks in the world, offering investment banking, commercial banking, asset management, and consumer financial services.
JPMorgan Chase currently has
1226 open roles
on FindRole.
Listed pay typically runs
$156,750–$215,000
across 5 roles with salary data.
The Quant Modeling Lead - Model Risk Governance Review - Vice President joins the Model Risk Governance and Review Consumer & Community Banking Operations & Digital Channels team. In this role, you independently assess and challenge models and AI systems supporting digital customer experiences and operational decision-making across chatbots, voice assistants, mobile applications, and web platforms. You will conduct independent model validation, evaluating conceptual soundness, implementation accuracy, and business suitability for statistical, machine learning, and AI systems. Your work involves reviewing regression, decision trees, neural networks, transformers, and agentic systems while assessing reliability, explainability, and bias. You will utilize Python, PyTorch, TensorFlow, XGBoost, and LightGBM to analyze LLM-based solutions and RAG architectures. You must translate complex technical findings into actionable recommendations to strengthen the firm's risk posture.
Lead independent model validation and governance activities for CCB Operations and Digital Channels.
Assess the conceptual soundness, accuracy, and business suitability of machine learning and AI systems.
Review diverse models including neural networks, transformers, recommender systems, and Generative AI solutions.
Evaluate agentic systems by analyzing intent routing, tool-use, guardrails, and end-to-end lifecycle considerations.
Perform performance evaluations using back-testing, stability analysis, drift analysis, and controlled experiments.
Assess risk postures for LLMs regarding reliability, explainability, bias, privacy, and prompt-injection resilience.
Translate complex technical findings into clear risk reports and presentations for technical and non-technical audiences.
Escalate material model issues and track remediation actions to closure.
What we're looking for
Master’s or PhD in Mathematics, Statistics, Computer Science, Engineering, Economics, Quantitative Finance, or a related field.
Minimum 6+ years of relevant hands-on experience in model risk, model validation, quantitative risk, or applied ML oversight.
Hands-on experience with applied AI/ML and strong understanding of GLMs, tree-based models, deep learning, transformers, LLMs, and modern AI techniques.
Strong foundation in statistics and machine learning including estimation, testing, bias/variance, overfitting, performance measurement, and uncertainty.
Experience with Python and ML frameworks such as PyTorch, TensorFlow, XGBoost, or LightGBM.
Excellent written and verbal communication skills with the ability to explain complex technical topics with clarity and precision.
Strong risk and control mindset with demonstrated ability to identify, assess, and escalate model/AI risks.
Experience with LLM technologies, RAG architectures, agentic AI systems, and conversational AI solutions (preferred).
Remote (Columbus, OH)21 days ago
$118,000–$222,000
Artificial Intelligence
Machine Learning
Generative AI
Python
SQL
Big Data
Data Visualization
Statistics
Unstructured Data
Agentic AI
Agile
Data Processing
Model Building
Risk Management