Senior Data Analyst

Worth AI, a forward-thinking company in the computer software industry, is looking for a detail-oriented and analytical individual to join their team as a Senior Data Analyst. At Worth AI, we strive to drive impactful decision-making through insights derived from data, while upholding values of one team, extreme ownership and creating raving fans both internally and for our customers.

As a Senior Data Analyst within our fintech organization, you will play a critical role in analyzing and optimizing the data that drives credit decisioning, underwriting, and fraud prevention models. You will work closely with Finance, Risk, Product, and Engineering teams to mine large and complex datasets, build dashboards, write business-critical queries, and help steer the company’s AI-driven underwriting strategy. This role requires both business acumen and technical proficiency, including a solid understanding of data engineering principles and the ability to deliver scalable analytics solutions in a regulated environment.

Responsibilities

• Mine and analyze large-scale credit, transactional, and behavioral datasets to identify trends, anomalies, and insights that inform credit risk, underwriting, fraud, and pricing strategies.
• Write performant SQL queries to extract, transform, and analyze data across structured and semi-structured sources (including loan-level, customer, and bureau data).
• Design, build, and maintain interactive dashboards and reporting tools to support Finance, Risk, Credit, and Executive teams in monitoring key performance indicators.
• Partner with Data, Finance,  Product and Engineering teams to define and track key metrics for credit performance, loss forecasting, and portfolio health.
• Support the development and ongoing evaluation of AI / ML models by providing exploratory data analysis, historical performance context, and business rule validation.
• Collaborate with data engineering and data science teams to ensure pipelines are reliable, secure, and meet both operational and regulatory standards.
• Automate recurring reports and analyses to improve visibility and reduce manual workflows across the credit and finance lifecycle.
• Contribute to model governance efforts by ensuring documentation, auditability, and transparency of data inputs used in risk models and decision engines.
• Stay informed on regulatory and compliance requirements related to data usage in financial services (e.g., Fair Lending, ECOA, GLBA) and align analytics practices accordingly.

Requirements

Benefits