Senior Data Science Researcher

Job description

**Honeycomb Insurance** At Honeycomb, we're not just building technology; we’re reshaping the future of insurance. In 2025, Honeycomb was recognized by Dun & Bradstreet as “[Top 10 Best Start Up Companies to Work For](https://www.duns100.co.il/en/rating/High_Tech/Best_Start_Up_Companies_to_work_for_Over_100_Employees)” in Israel, named by LinkedIn as “[Top 10 Startups in Chicago](https://www.linkedin.com/posts/honeycombinsurance_linkedin-top-startups-2025-the-10-companies-activity-7389324752848498688-OyDI/)”, and Newsweek’s "[Greatest Startup Workplaces in America, 2025](https://rankings.newsweek.com/americas-greatest-startup-workplaces-2025)". Through the first half of 2026 we’ve been recognized on Inc. Magazine’s "[Best Workplaces](https://www.linkedin.com/posts/honeycombinsurance_best-workplaces-2026-the-companies-unlocking-activity-7467578217680384001-eXF7/)" List and Forbes "[Fintech 50](https://www.linkedin.com/posts/honeycombinsurance_newcomers-to-the-fintech-50-2026-activity-7430317060871712768-Rdv6/)". How did we earn these honors? Honeycomb is a rapidly growing global startup, generously backed by top-tier investors and powered by an exceptional team of thinkers, builders, and problem-solvers. Dual-headquartered in Chicago and Tel Aviv (R&D center), and with 6 offices across the U.S., we are reinventing the commercial real estate insurance industry, an industry long overdue for disruption. Just as importantly, we ensure every employee feels deeply connected to our mission and one another. With over $100B in insured assets, Honeycomb operates across 23 states, covering more than 65% of the U.S. population and increasing its coverage. If you’re looking for a place where innovation is celebrated, culture actually means something, and smart people challenge you to be better every day - Honeycomb might be exactly what you’ve been looking for. - **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.