Senior Data Scientist

Cribl · Enterprise · CA · People

Senior Data Scientist to join and grow internal data science practice, using predictive models and advanced analytics to provide foresight into business outcomes. Role involves end-to-end data science initiatives, building and operationalizing ML models, evaluating model performance, conducting deep-dive analyses, applying statistical modeling and AI-assisted analytics, and communicating findings to stakeholders. Requires experience in experiment design, causal inference, predictive modeling, SQL, Python, and working with large datasets. Familiarity with leveraging LLMs as tools is a plus.

What you'd actually do

  1. Own end-to-end data science initiatives that translate ambiguous business questions into clear analytical and modeling problems, from scoping through implementation.
  2. Build, operationalize, and continuously improve machine learning models in partnership with Data Engineering to ensure scalable training & evaluation workflows in production.
  3. Evaluate and interpret the performance of ML models (e.g., classification, recommendation), helping the team reason through trade-offs, limitations, and business implications of model-driven decisions.
  4. Conduct deep-dive analyses using advanced statistical methods to surface actionable insights to stakeholders and positively influence org-wide decision quality.
  5. Apply statistical modeling, experimentation, and AI-assisted analytics to expand how Cribl uses data to answer challenging questions.

Skills

Required

  • 5+ years of experience in data science or applied statistics
  • Strong background in experiment design, causal inference, predictive modeling
  • Advanced SQL and Python skills
  • experience working on large, messy, multi-source datasets within a modern data stack
  • Proven ownership of end-to-end data science and ML solutions
  • Ability to work effectively in ambiguous, fast-changing environments
  • Excellent communication, including the ability to present complex findings to non-technical audiences

Nice to have

  • Familiarity with AI-assisted workflows, including leveraging LLMs as tools to accelerate analysis, surface insights, or augment modeling work.

What the JD emphasized

  • track record of driving data science projects with meaningful business impact
  • Proven ownership of end-to-end data science and ML solutions
  • work effectively in ambiguous, fast-changing environments

Other signals

  • AI Platform for Telemetry
  • predictive models
  • machine learning models
  • AI-assisted analytics
  • leveraging LLMs as tools