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# SAS Data Modeling Expert - Credit Risk

- **Company:** Tiger Analytics
- **Location:** Columbus, OH, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Data Analysis, Big Data, Logistic Regression, Machine Learning, SAS (Software), Transaction Data, Model Validation, Build Management
- **Published:** November 7, 2026
- **Apply:** https://www.careerbuilder.com/job-details/sas-data-modeling-expert-credit-risk-columbus-oh--4e8183f5-de66-4d38-ae33-7e780aba3b92

## About the Role

Data Modeling, Statistical Analysis System (SAS), Credit Risk, Leadership, Data Science, Machine Learning, Artificial Intelligence (AI), Market Research, Consulting, Channel Strategies, * 6+ years in data science / quantitative modeling, with meaningful experience in financial services, banking risk analytics.

 * Hands-on expertise building classification and logistic regression models for segmentation and risk scoring.
 * Strong SAS proficiency for large-scale data analysis and modeling in a production/regulated environment.
 * Direct experience with transaction monitoring, alert tuning, or scenario optimization.
 * Familiarity with model risk governance and validation expectations in a regulated banking setting
 * Excellent stakeholder communication; able to explain modeling decisions to non-technical audiences and defend them to reviewers.
 * Experience coordinating or reviewing work delivered by an offshore team.

## Description

Discovery & data analysis

 * Work with client data and technology stakeholders to secure and validate access to the SAS environment holding historical transaction data.
 * Profile ~12 months of historical alert and transaction data to quantify volume, conversion, and false-positive drivers across targeted rules.
 * Lead deep-dive scenario analysis on priority areas (Zelle, cash monitoring), identifying where broad cutoffs can be replaced with risk-based segmentation.

Model design & development

 * Design and build classification and logistic regression (logit) models to segment monitored populations and construct more precise risk scenarios.
 * Translate analytical findings into defensible rule/scenario recommendations, with clear rationale for thresholds and segment definitions.
 * Partner with offshore resources, setting analytical direction, reviewing outputs, and ensuring consistency and quality across the team.

Governance & stakeholder alignment

 * Ensure model logic, assumptions, and segmentation approaches align with the bank's internal risk governance standards.
 * Prepare documentation and supporting evidence to enable review by the internal model validation team.
 * Serve as the day-to-day onshore contact for the client, communicating progress, findings, and trade-offs to both technical and business stakeholders.