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 foster. s 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.
Job 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 explain ability, 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
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Required Qualifications, Skills, and Capabilities
- 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
Preferred qualifications, skills, and capabilities
- 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.
