Sr. Data Scientist (hybrid, Ire)

CrowdStrike CrowdStrike · Enterprise · Dublin, Ireland

Senior Data Scientist at CrowdStrike focused on building the next generation of risk scoring and prioritization in Exposure Management. The role involves leading research strategy, driving innovation in risk prioritization, productionizing algorithms, and enabling observability. Requires strong technical leadership, experience in training models at scale, and expertise in Python and deep learning frameworks.

What you'd actually do

  1. Lead and architect the research strategy for risk and posture scores, utilizing data from across CrowdStrike
  2. Drive innovation in risk prioritization, including novel approaches to data classification
  3. Work hand-in-hand with software engineers to productionize new algorithms and models
  4. Enable observability of existing algorithms to inform future updates
  5. Lead cross-functional initiatives and collaborate with senior leadership to align technical strategy with business goals

Skills

Required

  • Bachelors’ Degree in Computer Science, Data Science, Statistics or a related field
  • 8+ years of applied machine learning / research experience
  • Advanced knowledge of Python
  • Advanced knowledge of Deep Learning frameworks
  • Advanced knowledge of cloud technologies
  • Strong technical leadership experience
  • Mentoring teams
  • Driving technical strategy
  • Outstanding communication skills
  • Ability to influence senior stakeholders

Nice to have

  • Patents or significant intellectual property contributions in cybersecurity or risk modeling
  • Track record of successful research-to-production implementations at scale
  • Experience with cybersecurity applications of machine learning

What the JD emphasized

  • demonstrated leadership in developing production-grade models
  • Deep expertise in training models / deployment at scale
  • Track record of solving complex technical challenges at scale
  • Track record of successful research-to-production implementations at scale
  • Experience with cybersecurity applications of machine learning

Other signals

  • risk scoring and prioritization
  • novel approaches to data classification
  • productionize new algorithms and models
  • cybersecurity applications of machine learning