Actuarial Data Scientist
About the role
- Bachelor's degree in statistics, mathematics, data science, or other quantitative field 5+ years of experience in data science, analytics, or actuarial modeling in insurance
- Working knowledge of P&C insurance in loss cost modeling
- Exposure to demand, underwriting, and/or claims modeling
- Strong background in statistical modeling: GLMs, regularization, tree-based ensembles (XGBoost, LightGBM), and model validation
- Knowledge of actuarial principles as they relate to insurance pricing
- Advanced proficiency in Python (pandas, scikit-learn, statsmodels) and SQLExcellent communication skills and ability to build trust with stakeholders at all levels
- Strongly preferred: Personal lines or property insurance experience
- Advanced degree (Master's) in quantitative discipline
- Actuarial credentials - ACAS/FCASExperience with modern data platforms, cloud infrastructure (AWS), and workflow orchestration (Airflow)
- Experience with catastrophe modeling, geospatial modeling, or climate analytics data
- Tools Power_BI Tableau Excel Domain Homeowners Personal_Lines
Before you apply
Applying takes about a minute. These four things decide how fast it moves after that.
Your profile is current
It's what we read first. Occupations, seniority and locations matter more than a long history.
Two examples you can talk through
Not a portfolio — just two pieces of work where you can explain the decisions and what you'd change.
A number in mind
What you're on now and what would make you move. We negotiate better when we know both.
Your notice period
Employers plan around it, and it's the question that stalls offers most often.
Once you apply, someone reads it and calls you before anything reaches the employer — usually within two working days.
