Sr. Data Scientist

CrowdStrike•Sunnyvale, CA

2d•$140,000 - $215,000•Hybrid

About The Position

CrowdStrike 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. This is a hybrid role based out of our office in Sunnyvale, CA, requiring 2 days per week on-site.

Requirements

8+ years of experience building and scaling large-scale AI/ML, search, retrieval, ranking in the cybersecurity space.
MS, PhD, or equivalent industry experience in Computer Science, Statistics, Physics, or a related quantitative discipline.
Deep expertise in semantic retrieval, vector search, ranking systems, recommendation systems, NLP/LLMs, RAG architectures, and agentic AI systems.
Strong understanding of retrieval and ranking trade-offs, experimentation methodologies, model evaluation, and operational excellence.
Experience operating large-scale production systems with demanding latency, scalability, reliability, and cost requirements.
Proven track record of delivering measurable business impact through AI and machine learning innovation.
Experience leading cross-functional initiatives across multiple organizations and stakeholder groups.
Ability to influence executive stakeholders and drive strategic technical decisions across large organizations.
Exceptional communication, leadership, strategic thinking, and mentoring skills.
Strong programming skills in Go, Python, SQL, and modern machine learning ecosystems.
Experience designing and deploying end-to-end ML solutions, including data acquisition, feature engineering, model development, evaluation, deployment, and monitoring.
Strong knowledge of experimentation frameworks, online evaluation, A/B testing, causal inference, and model validation methodologies.
Proficiency in Anomaly Detection, MITRE entities, Detection Engineering, Triage and Investigation.
Experience with modern AI infrastructure, large-scale data processing, distributed systems, and production ML platforms.
Ability to communicate data-driven insights, uncertainty, assumptions, and trade-offs to technical and non-technical audiences.

Nice To Haves

Published research, patents, open-source contributions, or recognized thought leadership in AI, machine learning, search, retrieval, or cybersecurity.
Experience building large-scale search, recommendation, retrieval, or agentic AI systems serving enterprise or consumer products.
Experience leading technical strategy for AI platforms, ML infrastructure, or AI-native product initiatives.
Demonstrated success driving zero-to-one innovation and scaling solutions to enterprise-wide adoption.

Responsibilities

AI, Search & Retrieval Strategy: 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.
AI/ML Architecture & Technical Leadership: 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.
Product & Business Impact: 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 solutions.
Communicate technical trade-offs, recommendations, and results clearly to both technical and executive audiences.
Technical Leadership: Lead highly ambiguous, multi-quarter initiatives spanning Product, Engineering, Data Science, and other stakeholders.
Influence organization-wide technical strategy, investment priorities, and long-term AI roadmap decisions.
Represent the organization in executive reviews and drive alignment on major technical and product initiatives.
Mentor junior scientists while fostering a culture of technical excellence and innovation.

Benefits

Market leader in compensation and equity awards
Comprehensive physical and mental wellness programs
Competitive vacation and holidays for recharge
Paid parental and adoption leaves
Professional development opportunities for all employees regardless of level or role
Employee Networks, geographic neighborhood groups, and volunteer opportunities to build connections
Vibrant office culture with world class amenities
Great Place to Work Certified™ across the globe
health insurance
401k
paid time off