Sr. Data Scientist; Remote

USA
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USA
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Full time

R29023

As a global leader in cybersecurity, Crowd Strike protects the people, processes and technologies that drive modern organizations. Since 2011, our mission hasn’t changed — we’re here to stop breaches, and we’ve redefined modern security with the world’s most advanced AI-native platform. We work on large scale distributed systems, processing almost 3 trillion events per day and this traffic is growing daily.

Our customers span all industries, and they count on Crowd Strike to keep their businesses running, their communities safe and their lives moving forward. We're proud to work for a mission-driven company leveraging AI to transform the way we work. Crowd Strikers drive their careers through flexibility and autonomy while also being expected to contribute to a culture of responsible AI adoption, experimentation, and innovation.

We use an AI-first mindset as a force multiplier to proactively and continuously accelerate execution, build expertise, uncover insights, and solve complex problems. We’re always looking to add talented Crowd Strikers to the team who have limitless passion, a relentless focus on innovation and a fanatical commitment to our customers, our community and each other.

Crowd Strike is seeking an exceptional Senior Data Scientist to join our NGSIEM Agentic AI team. In this role, you will design, build and deploy advanced AI systems that analyze and prioritize millions of security events per second, enabling organizations to rapidly identify and respond to emerging threats. You will develop intelligent models for anomaly detection, event classification, ranking, correlation, and automated security reasoning.

As a key contributor, you will take ownership of complex technical challenges, drive innovation across the AI stack, and help shape the next generation of AI-powered security operations.

• Lead the architecture and technical strategy for large-scale retrieval, ranking, vector search, RAG, and anomaly detection systems operating at enterprise scale.

• Drive innovation across LLMs, AI-powered discovery, personalization, and autonomous AI workflows.

• Define the future direction of AI-driven reasoning, ranking and investigation capabilities across the platform.

• Own end-to-end AI/ML architecture, including data pipelines, feature engineering, model development, deployment, monitoring, evaluation, and continuous improvement.

• Establish scalable architecture patterns, engineering standards, experimentation frameworks, and operational best practices across multiple teams.

• Drive foundational investments and technical direction across AI platforms, retrieval infrastructure, model-serving systems, and ML tooling.

• Balance model quality, customer experience, latency, scalability, reliability, security, and infrastructure cost when making architectural decisions.

• Define evaluation methodologies, experimentation strategies, and success metrics to assess product, model, and business impact.

• Partner closely with Product, Engineering, Security, Infrastructure, and Operations teams to identify high-value opportunities and deliver scalable AI