• About The Role
  
    We are looking for a Senior Data Scientist to join Honeycomb's AI team, the team behind underwriting decisions, quote-time risk scoring, and portfolio analytics.
  
    In this role, you will own entire research directions: framing the problem, deciding what's worth measuring, designing the approach from first principles, and turning
    the result into production signal. The work is production-oriented applied research on hard, real-world datasets where standard recipes don't apply and the right method
    often has to be invented for the problem.
  
    What You'll Do
  
    - Own open-ended research directions end-to-end - from a vague business question to a deployed, validated signal.
    - Work across the team's core research areas — risk modeling, environmental and catastrophe modeling, and behavioral modeling - translating complex real-world processes
      into reliable predictive signal.
    - Deeply investigate existing production models - understand what they actually learn, where they break, and where the headroom is; identify and ship optimizations
      grounded in that understanding.
    - Design approaches from first principles when off-the-shelf methods don't fit the data.
    - Build rigorous evaluations and own the result in production, not just the notebook.
    - Document research clearly so findings are reproducible, auditable, and compound over time.
  
    Requirements
  
    - MSc. or PhD in Statistics, Applied Math, Physics, or a closely related quantitative field.
    - Real research experience - a track record of driving original investigations end-to-end (academic research, a research-heavy PhD, industry R&D, or equivalent), not
      only applied ML delivery.
    - Strong mathematical or statistical problem-solving instincts - able to model a messy real-world system from scratch, not just apply a library.
    - Production deployment experience: monitoring, CI/CD, data validation, reproducibility.
    - Ability to independently initiate, plan, and drive entire research directions.
    - Team player, positive, driven, independent, fast learner. 

         Advantages

         - Depth in classical statistics, causal inference, or applied probability.
         - Deep learning experience.
         - Clean coding and repository-maintenance habits.