Applicants in San Francisco: Qualified applications with arrest or conviction records will be considered for employment in accordance with the San Francisco Fair Chance Ordinance for Employers and the California Fair Chance Act.
Note: By applying to this position you will have an opportunity to share your preferred working location from the following: San Francisco, CA, USA; Reston, VA, USA.
US: $147000 - $211000 (USD) + 15% bonus target + equity + benefits
Learn more about benefits at Google.Google is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. See also Google's EEO Policy and EEO is the Law. If you have a disability or special need that requires accommodation, please let us know by completing our Accommodations for Applicants form.
Note: By applying to this position you will have an opportunity to share your preferred working location from the following: San Francisco, CA, USA; Reston, VA, USA.
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.
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. In this role, you will partner with subject matter experts on the front lines defending against advanced threat actors, iteratively develop new capabilities, and work closely with our engineering team to deploy and maintain them. Our team works on a wide variety of cybersecurity challenges including agentic security operations, threat intelligence contextualization, security-tuning LLMs, dark web threat detection, and more. We encourage sharing impactful learnings through publications at academic conferences and talks at industry venues.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $147000 - $211000 (USD) + 15% bonus target + equity + benefits
Learn more about benefits at Google.
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.