Data Scientist, Cloudforce One Threat Intelligence

Cloudforce One is Cloudflare's threat operations and research team, responsible for identifying and disrupting cyber threats ranging from sophisticated cyber criminal activity to nation-state sponsored advanced persistent threats (APTs). Cloudforce One works in close partnership with external organizations and internal Cloudflare teams, continuously developing operational tradecraft and expanding ever-growing sources of threat intelligence to enable expedited threat hunting and remediation. Members of Cloudforce One are at the helm of leveraging an incredibly vast and varied set of data points that only one of the world's largest global networks can provide. The team is able to analyze these unique data points, at massive scale and efficiency, synthesizing findings into actionable threat intelligence to better protect our customers.The team's core disciplines are data engineering, data science, devops, and security. We use data science and machine learning to process large volumes of data and build threat intelligence for Cloudflare's customers

About the Role

We're seeking a Data Scientist to join our team and drive the analytical rigor behind our cyber threat intelligence efforts. In this highly impactful, hands-on role, you will be instrumental in researching, developing, and applying advanced analytical techniques and statistical models to massive datasets from our global network. Your work will uncover patterns, identify anomalies, and build predictive insights to detect and neutralize sophisticated cyber threats. You'll directly enhance our Cloudforce One offering by transforming raw data into critical, real-time threat intelligence, providing unparalleled protection and actionable insights to our customers. If you are passionate about applying sophisticated data science to solve complex cybersecurity challenges at an unprecedented scale, we want you on our team.

The ideal candidate will possess a strong foundational knowledge of data science principles and statistical modeling. Day-to-day responsibilities include:

• Collaborate with other Data Scientists and Machine Learning Engineers to design and implement analytical approaches that inform scalable threat detection models.
• Partner with threat intelligence analysts to understand emerging attack techniques and leverage data to develop novel detection strategies and expose findings.
• Perform in-depth data analysis to identify trends, relationships, and anomalies within vast security datasets.
• Develop, validate, and refine statistical and machine learning models for threat detection and intelligence generation.
• Monitor the performance of analytical models and threat intelligence pipelines, continuously identifying opportunities for improvement and refinement.
• Investigate and resolve data-related issues in production environments, ensuring the accuracy and integrity of our threat intelligence.

Examples of desirable skills, knowledge, and experience:

• Thorough understanding of statistical modeling, hypothesis testing, and various machine learning algorithms, including their strengths and weaknesses across different data types.
• Desire to see a data-driven project through all the way from initial research and experimentation, through model validation, to the delivery of actionable insights and automated processes.
• Demonstrated ability to present complex analytical findings clearly and concisely, and actively solicit and incorporate feedback.
• Proven ability to deliver high-quality analytical work (what) with strong collaborative and problem-solving behaviors (how).

Bonus Points

These are skills that are beneficial and may be required in the role; we are happy to support your learning on the job, but prior experience is a plus:

• Experience with large-scale data processing frameworks (e.g., Spark, Flink).
• Experience with time series analysis, anomaly detection, or graph analytics in a security context.
• Proficiency in data visualization tools and techniques to effectively communicate complex findings.
• A basic understanding of the cyber threat landscape and technical Indicators of Compromise (IOCs).
• Experience with Natural Language Processing (NLP) for analyzing unstructured security data.