Field Examination- Data & Analytics-Analyst

JPMorgan Chase & Co.•Fort Worth, TX

About The Position

Asset Based Lending (ABL) is a form of financing that provides asset-based loans to a wide range of companies, particularly those with asset-rich balance sheets and working capital needs. ABL supports businesses across diverse industries such as Consumer & Retail, Industrials, Metals & Mining, Oil & Gas, and Tech/Media/Telecom, etc. ABL offers full-service solutions including originations, syndications, portfolio management, collateral monitoring, and loan servicing for both syndicated and sole-lender transactions. As a Field Exam Data & Analytics Analyst in Risk Management and Compliance, you will be dedicated to standardizing processes, automating workflows, and leveraging AI and automation capabilities (e.g., automated data ingestion from disparate sources, AI-assisted workpaper review, etc.) to achieve measurable results for clients and internal stakeholders. The role of an ABL Field Exam Analyst, Data & Analytics focuses on generating structured data capture, trend analyses, and operational reporting inputs that enable centralized insights and improved risk identification. The team fosters an environment that values intellectual curiosity, critical thinking, and a passion for enabling analytics-driven decision making. You will be a founding member of the Field Exam D&A team, responsible for assisting in the design and development of the data model, building centralized data storage, and creating the AI-augmented analytics layer from the ground up.

Requirements

SQL or SQL-like languages proficiency
Foundational understanding of data modeling and data pipeline concepts (how data moves from source to reporting layer)
Comfort working with AI-assisted tools and willingness to learn prompt engineering, LLM-based workflows, and agentic automation concepts
Strong interpersonal and relationship development skills; effective business writing skills.
Proficiency in Microsoft Suite of products such as PowerPoint, Excel, Visio and Project
Strong analytical skills with attention to detail and accuracy; intellectual curiosity and problem solving.
Ability to think critically while working in a fast-paced environment.
Experience in developing complex business analysis models by being resourceful, consulting with others and considering alternatives

Nice To Haves

Commercial lending, field exam, accounting, or auditing experience.
CPA (Certified Public Accountant) and/or CFE (Certified Fraud Examiner).
Technical skills to access complex data sources to solve problems; examples include Alteryx, Qlik Sense, Python, BI Tools (PowerBI, Tableau), etc.
Minimum 1–3 years of work experience; at least 1 year with MIS or Analytics
Experience with cloud-based data platforms (Snowflake, Databricks, Starburst, AWS Redshift, or similar)
Hands-on experience building data models, schemas, and ETL pipelines
Familiarity with standardized reporting and operational metrics concepts (status tracking, workload/productivity measures) and interest in analytics enablement.
Bachelor’s degree in Accounting, Finance, or Data Analytics strongly preferred; other majors considered based on experience and relevant coursework.

Responsibilities

Build and maintain operational and strategic KPI dashboards
Analyze client accounts receivable, inventory, and accounts payable data and historical performance datasets to identify trends, anomalies, and performance drivers
Support standardized data capture and consistent documentation to improve downstream reporting and insights
Automate recurring reports and improve reporting efficiency
Build and maintain client’s collateral monitoring model
Support the creation/testing of standardized LLM prompt packs for examiner workflows to drive targeted risk identification and efficiency.
Collaborate on AI-assisted reconciliation and exception-triage workflows
Evaluate and prototype agentic automation use cases (e.g., multi-step data extraction from Excel files, cross-system validation, anomaly surfacing)
Build QA and feedback loops for AI-generated outputs to ensure explainability, accuracy, and compliance with model risk and audit standards
Collaborate with various platform, product, and process owners across the ABL ecosystem to creatively integrate data and insights
Establish data quality, validation, and governance standards