SAS Data Modeling Expert - Credit Risk

Tiger Analytics; North Carolina; On-site; USD 120,000 - 180,000; Full time

Job summary

Tiger Analytics is seeking a hands-on data modeling expert to lead the onshore delivery of a transaction monitoring optimization engagement for a large US bank. The role focuses on reducing false positives by moving from broad rule cutoffs to statistically grounded segmentation and classification models.

You will be the primary onshore contact, navigate the client's data environment, drive analysis, develop models, and coordinate a small offshore team while ensuring alignment with the bank's

Qualifications

• 6+ years in data science / quantitative modeling in financial services.
• Hands-on expertise building classification and logistic regression models.
• Strong SAS proficiency for large-scale data analysis and modeling.
• Direct experience with transaction monitoring, alert tuning, or scenario optimization.
• Familiarity with model risk governance and validation expectations in banking.
• Excellent stakeholder communication; able to explain modeling decisions to non-technical audiences.
• Experience coordinating or reviewing work delivered by an offshore team.

Responsibilities

• Discovery & data analysis: access SAS environment holding historical data and profile ~12 months of data.
• Model design & development: design and build classification and logit models; translate findings into recommendations.
• Governance & stakeholder alignment: ensure model logic aligns with risk governance; prepare documentation; onshore client liaison.

Skills

Data modeling

Logistic regression

Classification modeling

Stakeholder communication

Offshore coordination

Regulatory experience

Tools

SAS

Job description

Tiger Analytics is an advanced analytics consulting firm. We are the trusted analytics partner for several Fortune 100 companies, enabling them to generate business value from data. Our consultants bring deep expertise in Data Science, Machine Learning, and AI. Our business value and leadership have been recognized by various market research firms, including Forrester and Gartner.

We are looking for a hands-on data modeling expert to lead the onshore delivery of a transaction monitoring optimization engagement for a large US bank. The focus is reducing false positives in high-volume, low-conversion monitoring rules by moving from broad rule cutoffs to statistically grounded segmentation and classification models. You'll be the primary onshore point of contact and navigating the client's data environment, driving analysis, developing models, and coordinating a small offshore team, while keeping the work aligned to the bank's model risk governance standards.

Key Responsibilities

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.

Required Qualifications

• 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.

Significant career development opportunities exist as the company grows. The position offers a unique opportunity to be part of a small, fast-growing, challenging and entrepreneurial environment, with a high degree of individual responsibility.

Tiger Analytics provides equal employment opportunities to applicants and employees without regard to race, color, religion, age, sex, sexual orientation, gender identity/expression, pregnancy, national origin, ancestry, marital status, protected veteran status, disability status, or any other basis as protected by federal, state, or local law.