A large global financial institution is seeking a hands-on Data Modeler to support AI, machine-learning, and predictive-analytics initiatives within a cyber-focused organization.
Despite the title, this is not a traditional database-modeling position centered on ERDs or schema design. The role is focused on building, testing, and validating predictive and behavioral models using Python, PySpark, Databricks, and large-scale distributed datasets.
The successful candidate will contribute across the model lifecycle, including development, evaluation, troubleshooting, validation, documentation, and production-readiness assessment.
RESPONSIBILITIES
Build and refine predictive, statistical, behavioral, and machine-learning models.
Develop modeling and testing logic using Python and PySpark.
Work with Databricks and distributed data-processing technologies.
Prepare, transform, and analyze model inputs and features.
Design and execute model-performance tests.
Independently validate model logic, assumptions, outputs, and behavior.
Analyze false positives, false negatives, thresholds, and unexpected patterns.
Investigate whether performance issues originate from data quality, features, transformations, methodology, or model logic.
Adjust and retest models based on validation findings.
Evaluate models against expected and unexpected behavioral patterns.
Document modeling decisions, testing methodology, validation results, limitations, and recommendations.
Explain technical methodology and findings to relevant stakeholders.
Support models as they progress toward production readiness.
QUALIFICATIONS
Hands-on experience developing predictive, statistical, machine-learning, or behavioral models.
Direct experience testing and validating models.
Demonstrated ownership across multiple stages of the model lifecycle.
Strong Python programming skills.
Practical PySpark experience.
Hands-on Databricks experience.
Experience working with large, complex, or distributed datasets.
Strong understanding of model evaluation and performance testing.
Ability to identify and troubleshoot data-quality, feature-engineering, and model-performance issues.
Strong quantitative and analytical problem-solving skills.
Ability to document and explain modeling and validation decisions clearly.
Ability to work onsite three days per week in Jersey City or Charlotte.
PREFERRED EXPERIENCE
Cybersecurity or insider-risk analytics.
Fraud or anomaly detection.
Behavioral or surveillance analytics.
Banking or financial-services experience.
Java.
Model-drift analysis.
Experience productionizing analytical models.
Experience developing models within a regulated enterprise environment.
Numbers & Facts
Location
Jersey City, NJ or Charlotte, NC, NJ
Skills
Analysis Skillsunmatched
Artificial Intelligence (AI)unmatched
Banking Servicesunmatched
Computer Programmingunmatched
Data Modelingunmatched
Data Qualityunmatched
Data Setsunmatched
Database Designunmatched
Documentation Modelsunmatched
Financial Servicesunmatched
Global Financial Marketsunmatched
Identify Issuesunmatched
Internet Securityunmatched
Javaunmatched
Logic Testingunmatched
Machine Learningunmatched
Model Validationunmatched
Performance Modelingunmatched
Performance Testingunmatched
Predictive Modelingunmatched
Problem Solving Skillsunmatched
Python Programming/Scripting Languageunmatched
Quality Assurance Methodologyunmatched
Quantitative Analysisunmatched
Risk Analysisunmatched
Surveillanceunmatched
Test Plan/Scheduleunmatched
Testingunmatched
Validation Documentationunmatched
Validation Testingunmatched
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