Quant Analytics Senior Associate

JPMorgan Chase & Co.•Columbus, OH

About The Position

At JPMorganChase, we are seeking team members who are eager to modernize how data is used to identify financial crime risk and improve customer due diligence outcomes. As a Quant Analytics Associate Sr within the Data & Analytics organization supporting AML/KYC operations, you will help move the team beyond traditional Excel-based, retrospective reporting toward proactive and scalable analytical solutions that identify emerging risks, data quality issues, process gaps, and customer trends earlier in the case lifecycle. In this role, you will focus on designing, building, and maintaining analytically ready datasets that can be used by analysts, operational stakeholders, AI agents, and natural-language query tools. You will partner closely with AML/KYC operations, product owners, data owners, technology teams, and other analytics professionals to transform customer, transaction, and activity data into governed, well-documented, AI-ready data assets. The successful candidate will bring strong SQL expertise, practical ETL experience, comfort with Python, curiosity about AI-assisted code creation, and a strong ability to independently investigate ambiguous data problems. This role is ideal for someone who wants to combine hands-on data engineering, analytics, controls awareness, and business problem solving to help modernize AML/KYC decision support.

Requirements

3+ years of related experience.
Bachelor's degree in a quantitative or related field required; Master’s degree preferred.
Excellent critical thinking and problem-solving abilities.
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.

Nice To Haves

Master’s degree preferred.
Experience with Alteryx and Tableau preferred.
Knowledge on how to integrate with APIs preferred.

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.