Data Research Scientists

San Francisco, CA /
Analytics /
CyberCube delivers the most comprehensive cyber insurance analytics platform for the insurance industry.

We are solely focused on solving the hardest cyber risk challenges with world-class analytics. Our team is composed of multi-disciplinary experts across data science, cyber security, software engineering, modeling and commercial insurance. CyberCube offers products for cyber risk aggregation modeling and insurance underwriting. CyberCube leverages the threat intelligence from the world’s leading cyber security company, Symantec, along with several other data sources.

 CyberCube is headquartered in San Francisco, California. We are backed by ForgePoint Capital (the world’s largest venture capital fund dedicated to cyber security early stage investing), HSCM (premier insurance and insurtech investor) and Symantec Ventures. 

Develop mathematical and statistical methods to collect, organize, and interpret data.  Build and enhance analytical data models and machine learning algorithms to identify cyber risk patterns within data.  May perform additional duties that are similar and consistent with stated position requirements.
Qualified candidates must possess a doctorate degree (PhD) in Mathematics, Statistics, Systems Engineering, or closely related field plus 1 year of experience.  Of the required experience, must have 1 year of experience in each of the following:

Of the required experience, must have 1 year of experience in each of the following:

    • Big data technology
    • Crime assessment and prediction models
    • End-to-end (front-end and back-end) demonstrations of machine learning models
    • Natural Language Processing and Sentiment Analysis
    • Temporal Machine Learning Modeling
Send resume to or
CyberCube Analytics, Inc.
58 Maiden Lane, 3rd Floor
San Francisco, CA 94108

CyberCube Analytics, Inc. is an equal opportunity employer. We don’t tolerate discrimination against age, gender, gender identity, gender expression, sexual orientation, race, color, nationality, ethnicity, religion, disability, veteran status, protected genetic information or political affiliation.