Data Modeler

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.