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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Scientist - **Company:** Berkshire Hathaway - **Location:** San Ramon, CA, United States - **Experience:** Expert - **Salary:** $100,000.0 - $160,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Artificial Neural Networks, Microsoft Azure, Big Data, Cluster Analysis, Data Cleansing, Data Mining, Programming Tools, Distributed Systems, Generalized Linear Model, Python (Programming Language), Machine Learning, NumPy, Tensorflow, SQL Databases, Unstructured Data, Feature Engineering, Pytorch, Apache Spark, Git, Pandas, Scikit Learn, Xgboost, Data Pipelines, Docker, Databricks - **Published:** September 11, 2026 - **Apply:** https://www.thejobnetwork.com/job/0dedb5c6-22cd-44f4-a80c-a1038e69a56d/data-scientist-senior-data-scientist-risk-modeling ## About the Role Educational Background * Advanced degree (Master's or Ph.D.) in Statistics, Actuarial Science, Applied Mathematics, Data Science, Engineering, or other equivalent quantitative discipline. * Strong academic foundation in probability theory, statistical inference, stochastic processes, and Bayesian statistics. Technical & Analytical Skills * Deep expertise in probability models commonly used in insurance (e.g., frequency-severity models, GLMs, loss distributions). * Strong applied statistics skills for: + Risk modeling + Predictive analytics + Pricing & underwriting analytics + Catastrophe exposure analysis * Proficiency in statistical and machine learning techniques, including: + Regression (GLM, GAM, GAMMs) + Time-series forecasting + Gradient boosting, random forests, and other tree-based models, neural networks models + Clustering and segmentation + Bayesian methods * Hands-on experience with key programming tools: + Python (NumPy, pandas, scikit-learn; PyTorch/TensorFlow a plus) + R (actuarial/statistical packages) + SQL for data extraction and manipulation + Familiarity with Git and Docker is a plus * Experience with big data and distributed computing (e.g., Spark, Databricks, AWS/GCP/Azure) is a plus. Preferred Domain Knowledge in Risk Modeling * Strong understanding of property & casualty insurance, including: + Exposure modeling + Loss distributions (e.g., Pareto, lognormal, gamma) + Catastrophe risk concepts and tail modeling + Portfolio risk aggregation, reinsurance structures, and risk metrics (AAL, PML, TVaR) Data Skills * Ability to work with large structured and unstructured datasets. * Expertise in data cleaning, transformation, and feature engineering. * Experience building automated data pipelines and scalable model workflows. Modeling & Communication * Ability to translate complex statistical concepts into actionable business insights. * Experience communicating results clearly to actuaries, underwriters, executives, and non-technical stakeholders. * Strong documentation and model governance discipline. Professional Competencies * Curious, analytical thinker with strong problem-solving ability. * Ability to work independently in ambiguous problem spaces. * Collaborative mindset - comfortable partnering with actuaries, underwriters, portfolio managers, and engineers. ## Description The successful candidate will be responsible for identifying and applying cutting-edge data science techniques to build a better view of the risk for multiple perils. You will work across functional areas and perils within the team, supporting the development of models for various natural catastrophes, natural hazards, building vulnerability, and man-made risks such as cyber, casualty, among others. In addition, you will conduct in-depth evaluation of vendor models, research and develop internal views of exposure and risks, consult on account-specific risk analyses, and develop internal tools to facilitate account underwriting decision-making and other related activities., * Evaluate and develop insights into large and diverse data sets from claims, hazard models, structural analysis, geospatial sources, and various other public/proprietary datasets. * Develop and maintain expertise in advanced data science, machine learning, and artificial intelligence techniques, and their application to understanding risk. * Work with domain experts across teams and perils to enhance our use of available data. * Propose and execute innovative solutions to insurance problems that directly impact BHSI underwriting decisions. ## Related Videos - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Vectorize all the things! 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