Research Data Scientist, Cybersecurity, Google Cloud

About the Job

The Google Cloud Gusto AI Data Science Research team develops innovative, data-driven solutions to today’s most challenging cybersecurity problems. By leveraging data derived from Google's unparalleled view of the threat landscape, our cross-functional, applied research team experiments and delivers impactful findings for both our customers and the broader cybersecurity industry.

Responsibilities

• Explore promising areas of future research at the intersection of cybersecurity and machine learning.
  
• Drive the research and development of new models and analytic products to solve cybersecurity problems.
  
• Develop models and analytics. Move from proof of concept to minimum viable product quickly and efficiently.
• Work closely with other engineering teams to develop scalable data pipelines, deploy models/analytics, and enact telemetry-driven model improvements over time.
  
• Communicate research results to stakeholders and the research community through documentation, white papers, peer-reviewed publications, and presentations.

Qualifications

Minimum qualifications:

• Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field.
  
• 3 years of work experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or a PhD degree.
  

Preferred qualifications:

• 3 years of experience developing and deploying machine learning and AI models in production settings.
• Experience at the intersection of security or fraud and ML.
• Experience applying a variety of unsupervised, semi-supervised, and supervised machine learning techniques, and the ability to turn big data into actionable intelligence.
• Experience building LLM-powered applications to automate the analysis and contextualization of complex data.
• Experience with the challenges in applying machine learning in a non-stationary and adversarial environment.
• Ability to evaluate, analyze, and improve machine learning models and LLM-driven systems.

Additional Information