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About This Role
Who are we?
A strategic and trusted insurance partner, Berkshire Hathaway Specialty Insurance (BHSI), provides a broad range of commercial property, casualty and specialty insurance coverages and outstanding service to customers and brokers around the world. Part of Berkshire Hathaway’s insurance operations, we bring our solutions to market with our stellar brand name, top\-rated balance sheet, and the expertise of our global team of professionals, who exude excellent capabilities and strong character.
We are a values\-based organization where respect, integrity, excellence, collaboration, and passion define who we are and how we do business. We value diversity of backgrounds, experience, and perspectives and strive to foster an inclusive environment that enables all our team members to bring their best selves to work. We are one team committed to building a culture where every teammate has the opportunity to contribute and be recognized. Want to be part of the team building the finest property, casualty and specialty lines insurance company in the world?
Learn more about our unique culture and history. Job Opportunity:
Berkshire Hathaway Specialty Insurance (BHSI) has an exciting opportunity for Data Scientist / Senior Data Scientist to join the Catastrophe Engineering and Analytics (CAT E\&A) team. CAT E\&A is an innovative and versatile technical team conducting catastrophe risk research and development and providing complex quantitative metrics that inform underwriting decisions across multiple lines of business.
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. Duties \& Responsibilities:
- 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.
Qualifications, Skills and Experience:
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.
BHSI Offers:
- A competitive package and exciting growth opportunities for career\-oriented teammates.
- A dynamic, action oriented, and thoughtful environment centered on always doing the right thing for our customers, teammates, and our other stakeholders.
- A purposely non\-bureaucratic organization that embraces simplicity over complexity and emphasizes individual excellence in a team framework.
- Benefits that support your life and well\-being, which include:
+ Comprehensive Health, Dental and Vision benefits
+ Disability Insurance (both short\-term and long\-term)
+ Life Insurance (for you and your family)
+ Accidental Death \& Dismemberment Insurance (for you and your family)
+ Flexible Spending Accounts
+ Health Reimbursement Account
+ Employee Assistance Program
+ Retirement Savings 401(k) Plan with Company Match
+ Generous holiday and Paid Time Off
+ Tuition Reimbursement
+ Paid Parental Leave
*The base salary range for this position in Atlanta, GA, Boston, MA, or San Ramon, CA is $100,000 to $160,000, along with annual bonus eligibility. Total compensation for a candidate is determined by their relevant skills, location, and experience. We value our teammates – both their capabilities and character – as demonstrated by our amazing culture.*
NOTE: Compensation will be commensurate with experience. This job description is not intended to be all\-inclusive. Team Member may perform other related duties as negotiated to meet the ongoing needs of the organization.
Salary Context
This $100K-$160K range is below the median for Data Scientist roles in our dataset (median: $155K across 226 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 3,708 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Berkshire Hathaway Specialty Insurance, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $192,890 based on 463 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($130K) sits 33% below the category median. Disclosed range: $100K to $160K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Berkshire Hathaway Specialty Insurance AI Hiring
Berkshire Hathaway Specialty Insurance has 1 open AI role right now. They're hiring across Data Scientist. Based in San Ramon, CA, US. Compensation range: $160K - $160K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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