Senior Quant Analytics Associate, AML/KYC Data Engineering

JPMorgan Chase & Co.; Kentucky; On-site; USD 110,000 - 150,000; Full time

Job summary

JPMorganChase is seeking a Quant Analytics Associate Sr within the Data & Analytics organization supporting AML/KYC operations. The role aims to modernize how data is used to identify financial crime risk and improve customer due diligence outcomes.

The successful candidate will design, build, and maintain analytically ready datasets for analysts, AI agents, and NLP tools, partnering with data owners and technology teams to convert customer and transaction data into governed, AI-ready assets.

Qualifications

• 3+ years of related experience.
• Bachelor's degree in quantitative field; Master’s degree preferred.
• Proficiency in SQL and Python.
• Experience with Alteryx and Tableau preferred.
• Knowledge on how to integrate with APIs preferred.
• Strong project management skills with the ability to manage multiple projects simultaneously.
• Familiarity with data mining, statistical modeling, machine learning, and other advanced analytics methods.
• Robust data management skills with a focus on ensuring data quality and organization.
• Demonstrated ability to independently make the decisions and judgements necessary to deliver quality analytics solutions.
• Effective communication skills, both written and verbal.
• Strong soft skills, including adaptability, emotional intelligence, and conflict resolution.
• Strong adherence to controls and regulatory requirements.
• Commitment to continuous learning and professional development.

Responsibilities

• Design, develop, and maintain analytically ready datasets for AML/KYC operations, including datasets that support proactive monitoring, case prioritization, customer review insights, trend detection, and data quality surveillance.
• Build scalable ETL and data transformation processes that convert raw source-system data into reliable, well-structured, reusable data products for the broader Data & Analytics team.
• Develop AI-ready metadata, business definitions, lineage documentation, data dictionaries, and semantic layers that enable AI agents, self‑service analytics, and natural‑language querying.
• Use Python/SQL as the primary tool for data extraction, transformation, profiling, validation, and performance optimization across large and complex datasets.
• Apply Python and AI‑assisted code generation tools to improve development speed, automate repetitive analytics tasks, create reusable utilities, and support exploratory data analysis.
• Collaborate with analytics and AI teams to ensure datasets are structured for downstream model development, AI‑agent orchestration, retrieval‑augmented workflows, and natural‑language business questions.
• Create data quality checks, reconciliation routines, exception monitoring, and validation frameworks to ensure ARDs are accurate, complete, timely, and fit for purpose.
• Prepare clear documentation and executive‑ready presentations summarizing data findings, ARD readiness, development progress, data quality risks, and recommended next steps.
• Provide ongoing production support for data assets, including troubleshooting data issues, answering stakeholder questions, managing enhancements, and communicating impacts of source‑system changes.
• Ensure analytics, data transformation, and AI‑enablement activities comply with applicable controls, model/data governance expectations, privacy requirements, and regulatory standards.
• Contribute to a culture of innovation by proposing new approaches for data modernization, metadata enrichment, AI enablement, and proactive risk identification.

Skills

SQL

Python

Alteryx

Tableau

APIs

Data mining

Statistical modeling

Machine learning

Data governance

Analytical thinking

Communication

Education

Bachelor's degree in quantitative field

Master's degree preferred

Tools

Alteryx

Tableau

APIs

Job description

JPMorganChase is seeking a Quant Analytics Associate Sr within the Data & Analytics organization supporting AML/KYC operations. The role aims to modernize how data is used to identify financial crime risk and improve customer due diligence outcomes.

The successful candidate will design, build, and maintain analytically ready datasets for analysts, AI agents, and NLP tools, partnering with data owners and technology teams to convert customer and transaction data into governed, AI-ready assets.