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# Lead Data Scientist

- **Company:** AgileEngine, LLC
- **Location:** Jacksonville, FL, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Cloud Computing Security, Data Visualization, R (Programming Language), Python (Programming Language), Machine Learning, Tensorflow, SQL Databases, Software Vulnerability Management, Pytorch, Data Analytics, Data Delivery
- **Published:** June 23, 2026
- **Apply:** https://www.dice.com/job-detail/a3f0ada8-7743-46e9-a2b6-6dad02d23a5f

## About the Role

If you're looking for a place to grow, make an impact, and work with people who care, we'd love to meet you!, You must be authorized to work for ANY employer in the US (e.g., s, TN visa holders, U4U with EAD), as we are unable to sponsor or take over employment visa sponsorship at this time;

 - 6+ years of commercial data science experience, operating completely autonomously with no required supervision;

 - Advanced proficiency in Python, R, and SQL;

 - Experience with machine learning frameworks (TensorFlow, PyTorch) and data visualization tools;

 - Prior commercial experience applying data science specifically to cybersecurity, vulnerability management, or financial risk models;

 - Upper-intermediate English level.

NICE TO HAVES

 - Familiarity with Cloud Security Posture Management (CSPM) data outputs (e.g., Wiz).

## Description

We are looking for a Senior/Lead Data Scientist to develop predictive models and quantitative analytics that power risk-based vulnerability prioritization within a large-scale enterprise security program. You will structure and analyze massive security telemetry datasets, build ML algorithms to eliminate scanning noise and false positives, and establish unified data dashboards to drive adoption of data-driven security governance. The role requires 6+ years of data science experience applied specifically to cybersecurity, vulnerability management, or financial risk models.

WHAT YOU WILL DO

 - Develop predictive models and quantitative analytics to prioritize vulnerabilities based on contextual risk and business impact;

 - Structure and analyze massive outputs of security data from across the enterprise ecosystem to support the ASPM framework;

 - Create machine learning algorithms or statistical models to automatically identify and filter out scanning noise and false positives;

 - Establish a unified data delivery structure and dashboards to drive the adoption of data-driven security governance.


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