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. We are looking for a Research Data Scientist to help us solve challenging cybersecurity problems and protect billions of customers by applying their expert knowledge of machine learning, statistics, and Generative AI.
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
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
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.