SAS Data Modeling Lead — Transaction Monitoring

Tiger Analytics Inc.; Columbus (OH); On-site; USD 120,000 - 180,000; Full time

Job summary

Tiger Analytics Inc. 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 focus is reducing false positives by moving from broad rule cutoffs to statistically grounded segmentation and classification models, with you as the primary onshore contact. You will coordinate a small offshore team, ensure adherence to model risk governance standards, and translate analytical findings into defensible

Qualifications

• 6+ years in data science / quantitative modeling within financial services
• Hands-on experience building classification and logistic regression models for segmentation and risk scoring
• Direct experience with transaction monitoring, alert tuning, or scenario optimization

Responsibilities

• Discovery & data analysis: access SAS environment with historical transaction data; profile ~12 months of data to quantify volume, conversion, and false-positive drivers across rules; lead deep-dive scenario analysis for risk-based segmentation.
• Model design & development: design and build classification and logit models to segment monitored populations; translate findings into defensible rule/scenario recommendations; coordinate with offshore resources for consistency and quality.
• Governance & stakeholder alignment: ensure model logic aligns with bank's risk governance; prepare docs for model validation; serve as day-to-day onshore client contact communicating progress and trade-offs to technical and business stakeholders.

Skills

Data science

Logistic regression

SAS

Transaction monitoring

Model risk governance

Stakeholder communication

Offshore coordination

Tools

SAS

Job description

Tiger Analytics Inc. 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 focus is reducing false positives by moving from broad rule cutoffs to statistically grounded segmentation and classification models, with you as the primary onshore contact. You will coordinate a small offshore team, ensure adherence to model risk governance standards, and translate analytical findings into defensible