Data Scientist III (remote, Rou)

CrowdStrike CrowdStrike · Enterprise · Romania · Remote

This role leads Applied Generative AI Research and establishes enterprise-wide standards for Generative AI implementation. The Data Scientist will provide technical leadership, drive innovation, and make strategic decisions about LLM architectures, training, and deployment at scale. They will also mentor teams, represent CrowdStrike through publications, and establish best practices and evaluation frameworks for GenAI adoption.

What you'd actually do

  1. Lead and architect the research strategy for Applied GenAI initiatives, establishing technical standards and frameworks for enterprise-wide implementation
  2. Drive innovation in GenAI applications across business units, developing novel approaches for diverse use-cases while ensuring consistent quality and performance
  3. Make strategic decisions about GenAI model architectures, training approaches, and deployment strategies that can scale across the organisation
  4. Mentor and guide Data Scientists while fostering a culture of excellence and knowledge sharing across teams
  5. Lead cross-functional initiatives to standardise GenAI practices, collaborating with senior leadership to align technical strategy with business goals and ensure successful adoption

Skills

Required

  • Advanced degree (PhD or Masters) in Computer Science, Data Science, or a related field
  • 5+ years of applied machine learning / research experience
  • demonstrated leadership in developing production-grade models
  • Deep expertise in LLM training / deployment at scale
  • Strong technical leadership experience, including mentoring teams and driving technical strategy
  • Advanced knowledge of Python, Deep Learning frameworks, and cloud technologies
  • Expert-level understanding of GPU technologies and optimisation techniques
  • Outstanding communication skills with ability to influence senior stakeholders
  • Track record of solving complex technical challenges at scale

Nice to have

  • Patents or significant intellectual property contributions in AI
  • Strong research portfolio with publications in leading AI journals and conferences
  • Experience with cybersecurity applications of machine learning
  • Track record of successful research-to-production implementations at scale
  • History of contributions to open-source ML projects

What the JD emphasized

  • production-grade models
  • LLM training / deployment at scale
  • technical leadership experience
  • solving complex technical challenges at scale

Other signals

  • Applied Generative AI Research
  • enterprise-wide standards for Generative AI implementation
  • technical leadership and strategic direction
  • accelerating the adoption and integration of AI capabilities
  • shape the technical vision
  • democratise AI
  • standardise and scale GenAI applications
  • bridge the gap between cutting-edge AI research and practical enterprise implementation