Senior Data Science Researcher

• About The Role
  
  We are looking for a Senior Data Science Researcher 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.