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Lead development of predictive models and analytics to prioritize vulnerabilities using large security telemetry. Build ML/statistical models to reduce scanning noise and false positives, structure enterprise security data, and create dashboards to drive data-driven security governance.
Top Skills:
Data Visualization ToolsPythonPyTorchRSQLTensorFlow
Software
Build predictive models and ML/statistical algorithms to prioritize vulnerabilities and filter false positives using large-scale security telemetry. Structure security data, create dashboards, and drive data-driven security governance adoption.
Top Skills:
PythonPyTorchRSQLTensorFlow
Fintech • HR Tech
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AgileEngine is an Inc. 5000 company that creates award-winning software for Fortune 500 brands and trailblazing startups across 17+ industries. We rank among the leaders in areas like application development and AI/ML, and our people-first culture has earned us multiple Best Place to Work awards.
WHY JOIN US
If you're looking for a place to grow, make an impact, and work with people who care, we'd love to meet you!
ABOUT THE ROLE
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.
MUST HAVES
- You must be authorized to work for ANY employer in the US (e.g., Green card holders, TN visa holders, GC EAD, H4 EAD, 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).
PERKS AND BENEFITS
- Professional growth: Mentorship, TechTalks, and personalized growth roadmaps.
- Competitive compensation: USD-based pay with education, fitness, and team activity budgets.
- Exciting projects: Modern solutions with Fortune 500 and top product companies.
- Flextime: Flexible schedule with remote and office options.
What you need to know about the San Francisco Tech Scene
San Francisco and the surrounding Bay Area attracts more startup funding than any other region in the world. Home to Stanford University and UC Berkeley, leading VC firms and several of the world’s most valuable companies, the Bay Area is the place to go for anyone looking to make it big in the tech industry. That said, San Francisco has a lot to offer beyond technology thanks to a thriving art and music scene, excellent food and a short drive to several of the country’s most beautiful recreational areas.
Key Facts About San Francisco Tech
- Number of Tech Workers: 365,500; 13.9% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Google, Apple, Salesforce, Meta
- Key Industries: Artificial intelligence, cloud computing, fintech, consumer technology, software
- Funding Landscape: $50.5 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Sequoia Capital, Andreessen Horowitz, Bessemer Venture Partners, Greylock Partners, Khosla Ventures, Kleiner Perkins
- Research Centers and Universities: Stanford University; University of California, Berkeley; University of San Francisco; Santa Clara University; Ames Research Center; Center for AI Safety; California Institute for Regenerative Medicine
