Note: The job is a remote job and is open to candidates in USA. Trusted Resource Underwriters is a reciprocal property insurer serving homeowners in storm-prone regions across Florida and other coastal U.S. markets. The Senior Data Scientist will lead predictive modeling initiatives, develop statistical and machine learning models, and translate analytical insights into practical business solutions across pricing, underwriting, retention, and other applications. The role also involves collaborating with business and technology teams to operationalize models and advance data science capabilities.
Responsibilities
- Lead end-to-end predictive modeling initiatives supporting areas such as pricing and rating, underwriting and eligibility, conversion, retention, and other business applications
- Apply statistical, machine learning, and deep learning techniques to develop supervised, semi-supervised, and unsupervised models based on business needs
- Partner with Product, Actuarial, Underwriting, and other business teams to translate analytical insights and model outputs into actionable business solutions
- Collaborate with Technology teams to operationalize and implement models in production environments
- Extract, clean, transform, and analyze data from a variety of structured and unstructured sources
- Apply strong modeling practices across feature engineering, data transformation, imputation, model selection, validation, evaluation, and tuning
- Develop well-organized, maintainable, and production-ready analytical code
- Clearly communicate modeling approaches, results, limitations, and business value to both technical and non-technical stakeholders
- Help advance TRUE's data science capabilities, tools, methodologies, and best practices as the organization continues to scale
Skills
- 5+ years of professional data science, predictive modeling, or advanced analytics experience, including at least 2 years operating at a Senior Data Scientist level
- Master's degree or higher in Statistics, Machine Learning, Computer Science, Applied Mathematics, Economics, Physics, or another quantitative discipline
- Strong understanding of statistical modeling and machine learning methodologies, with demonstrated experience applying them to real-world business problems
- Experience developing and applying Generalized Linear Models (GLMs) is important for this role
- Advanced programming skills in Python and SQL; experience with R is highly valued
- Strong experience working with large, complex datasets and extracting, cleaning, transforming, and preparing data for modeling
- Solid understanding of software development and data science best practices, including code organization, reproducibility, version control, model validation, and production readiness
- Experience with version control systems, preferably Git
- Experience with cloud-based data science and machine learning environments; AWS and SageMaker experience preferred
- Bayesian modeling
- Experimental design and causal inference
- Strong command-line skills
- Experience with NoSQL or graph databases
- Scala or Java experience
- Experience developing performant APIs and consuming APIs at scale
- Experience deploying, monitoring, and maintaining machine learning models in production
- Familiarity with modern AI-assisted development tools, including Claude Code
- You should be comfortable working in an environment where priorities can evolve quickly, solutions are not always predefined, and data science is expected to have a direct impact on business decisions
- The right candidate will combine technical depth with intellectual curiosity, strong business judgment, and the ability to collaborate effectively across disciplines
- P&C insurance experience strongly preferred; homeowners insurance experience is a plus
- Exposure to deep learning methodologies and applications is a plus
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