Data Scientist, Security

Build machine learning models to find interesting data patterns, recognize and/or predict potentially malicious behavior, reduce risks associated with insider threats, and streamline incident response.

We’re looking for a data scientist with security engineering experience who is excited about applying their analytical skills to understand user behavior and influence decision making.  If you are naturally data curious, enjoy deriving insights from data, and motivated by the opportunity to develop models from the ground up that significantly impact the business, we want to hear from you!

Data security is of paramount importance at Stripe, and you will work with our Insider Threat Detection team, which is committed to promoting data security and protecting Stripe from internal and external threats to its assets and infrastructure.  We’re looking for talented candidates who can leverage data science to build out detection capabilities with an emphasis on user behavior, information misuse, and insider threat intelligence.  Your work will be critical to reducing risks and promoting trust and integrity within Stripe.

You will:

• Research, develop, design, and build models for threat detection, guiding processes for signal ingestion, data analytics, and automation to improve detection and investigation of potentially malicious activity.
• Work cross-functionally with data science, software development, and security engineering teams to architect solutions for analyzing security events data at scale and protecting Stripe networks, systems, and data from insider threats.
• Build statistical, machine learning, and simulation models on large datasets, including unstructured data from disparate sources.
• Drive the collection and processing of new data and the enrichment of existing data sources (network and/or host-based telemetry).
• Develop technical and functional requirements to deploy novel detection capabilities that mitigate emergent and current threats.
• Provide actionable insights to stakeholders to help identify, prevent, and detect anomalous or abusive patterns of user behavior.

We’re looking for someone who has:

• 5+ years experience working with behavioral models and analyzing large data sets to solve problems
• A PhD or MS in a quantitative field (e.g., Applied Mathematics, Computer Science, Statistics, Engineering, Natural Sciences)
• Existing experience with network security, digital forensics, or security incident response
• Expert knowledge of Python and SQL, and familiarity with other programming languages (R, Go, Scala)  
• Strong knowledge of statistics and machine learning
• Ability to communicate results clearly and focus on impact
• Ability to think creatively and holistically about reducing risk in a complex environment
• Passion for mentoring others and building a data science and security community

Nice to haves: 

• Familiarity with common network observability or security software (Uptycs, Iceberg, etc.)
• Experience with data-distributed tools (Scalding, Spark, Hadoop, Pig, etc.)
• Knowledge of network protocols (DNS or HTTPS) and understanding of cloud computing services/deployment architecture
• Experience in one or more of the following areas: user and entity behavior analytics (UEBA), security information event management (SIEM), data loss prevention (DLP), enterprise risk management (ERM), fraud detection, or identity and access management (IAM)