below marketmost similar roles pay hereabove market
This listing doesn't post a salary. Most similar roles pay
$139,500–$196,812.
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 Emerging Risk Strategic Analytics, Senior Associate joins the Risk Management and Compliance team as a Risk Intelligence & AI Lead in consumer banking Risk Insights. This role involves owning the development of AI-powered risk intelligence capabilities to monitor consumer and small business health, detect emerging risks, and deliver actionable insights to senior leadership. You will lead the design and deployment of LLM-driven solutions, agentic AI workflows, and advanced analytical tools to automate research and executive narratives. Key responsibilities include building production-grade solutions using Python, SQL, and machine learning frameworks like PyTorch, TensorFlow, Hugging Face, and LangChain. You will translate complex macroeconomic, credit, and portfolio trends into strategic recommendations while ensuring compliance with model governance, security, and responsible AI standards across large structured and unstructured datasets.
Develop and maintain risk intelligence capabilities to monitor consumer and small business health using internal and external data.
Lead the end-to-end development of Generative AI and Agentic AI capabilities, including architecture design and prompt engineering.
Build AI-powered solutions to automate emerging risk detection, narrative generation, and executive reporting workflows.
Translate complex macroeconomic and portfolio trends into actionable insights and strategic recommendations for senior leadership.
Partner with Technology and Governance teams to ensure all AI solutions comply with model governance and responsible AI standards.
Build automated research pipelines that synthesize structured and unstructured data into high-quality risk intelligence outputs.
Develop scalable prototypes and proof-of-concept solutions to demonstrate the value of new AI capabilities to stakeholders.
What we're looking for
Bachelor's degree in Computer Science, Data Science, Statistics, Economics, Engineering, or a related quantitative field.
Master's degree or PhD in a quantitative discipline such as Computer Science, Machine Learning, Statistics, Economics, or Computational Finance (preferred).
3+ years of experience in data science, risk analytics, AI/ML engineering, or a related quantitative discipline within financial services or a similarly complex, regulated industry.
Experience developing and deploying Generative AI or LLM-based solutions, including prompt engineering, RAG, fine-tuning, or agent-based architectures.
Strong proficiency in Python and SQL with experience working with large-scale structured and unstructured datasets.
Experience building automated data pipelines, reporting workflows, or analytical tools that operate at production scale.
Solid understanding of consumer credit risk, macroeconomic indicators, labor market dynamics, or portfolio performance analytics.
Knowledge of machine learning frameworks like PyTorch, TensorFlow, Hugging Face, or LangChain.
Experience with agentic AI frameworks, multi-agent orchestration, or autonomous workflow design (preferred).
Experience with cloud-based AI/Machine Learning platforms like AWS SageMaker or Azure ML (preferred).
Background in natural language processing (NLP) applied to financial documents, earnings reports, regulatory filings, or news analytics (preferred).
Python
AWS
Spark
H2O
SQL
Scala
R
Machine Learning
NLP
Generative AI
Conda
Deep Learning
Time Series
Clustering
Classification
Sentiment Analysis
Statistical Modeling
Big Data
Machine Learning
Data Science
PyTorch
TensorFlow
Scikit-Learn
Spark
Flink
Kafka
Databricks
SaaS
API
LLM
Big Data
Model Explainability
Drift Analysis
Adversarial ML