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About This Role
Overview:
If you're looking for the stability of a profitable, growing company with the entrepreneurial spirit of a startup, we're hiring. SageSure, a leader in catastrophe\-exposed property insurance, is seeking a Data Scientist to join our team. In this role, you will combine geospatial analytics, statistical modeling, machine learning, and software development to solve complex underwriting and risk management challenges.
You will develop scalable analytical tools, leverage spatial, property, policy and claims data, automate complex workflows, and work within a cross\-functional team – including Product Development, Underwriting, Cat Modeling, Data and Risk Management \- to support profitable growth.
The ideal candidate enjoys solving difficult business problems through data science, thrives working with large and complex datasets, and has experience applying technologies alongside modern analytics and AI techniques.
What you'd be doing:
- Deliver analytical insights that support business decisions as a member of a cross\-functional team — including Product Development, Underwriting, Cat Modeling, Data and Risk Management.
- Design and develop statistical, geospatial, and machine learning models that improve underwriting performance and portfolio management.
- Analyze spatial, exposure, catastrophe, and loss data to identify trends, concentrations of risk, and business opportunities.
- Execute predictive analytics, machine learning, and AI models that support pricing, underwriting, and operational efficiency.
- Apply statistical techniques including regression, clustering, classification, time series analysis, and causal inference to solve business problems.
- Support building software applications and analytical tools that automate underwriting and risk analysis.
- Build reusable data pipelines and automate data collection, cleansing, transformation, to support underwriting, product development and risk management.
- Design and maintain geospatial datasets using internal and third\-party data sources.
- Develop software applications and APIs that support underwriting analytics and business decision making.
- Mentor junior team members and promote best practices for AI use within the department.
- Perform other duties and special projects as assigned
We are looking for someone who has:
- Bachelor's degree in Data Science, Computer Science, Statistics, Mathematics, Geographic Information Systems (GIS), Engineering, Information Science, or a related quantitative discipline.
- 5 to 7 years of experience in data science, analytics, software development, or geospatial analytics.
- Experience with statistical modeling techniques and building predictive models using Python, SAS or R, and strong statistical modeling techniques
- Experience developing solutions using SQL and relational databases.
- Experience working with large structured and unstructured datasets.
- Experience using GIS platforms such as ESRI ArcGIS, ArcGIS Pro, ArcObjects, or equivalent geospatial technologies.
- Experience developing automated data processing and analytical workflows.
- Experience building software applications or analytical tools that support business operations.
- Strong problem\-solving and communication skills
Highly preferred candidates also have:
- Experience in Property \& Casualty insurance.
- Familiarity with catastrophe models and exposure management.
- Experience with geospatial statistics and spatial modeling.
- Experience with AI and Generative AI technologies.
- Experience with C\#, .NET, or software engineering principles.
About SageSure:
Named among the Best Places to Work in Insurance by Business Insurance for four years in a row (2020\-2023\), SageSure is one of the largest managing general underwriters (MGU) focused on catastrophe\-exposed property in the US. Since its founding in 2009, SageSure has experienced exceptional growth while generating underwriting profits for carrier partners through hurricanes, wildfires, and hail. Available in 16 states, SageSure offers more than 110 home, flood, and commercial products on behalf of its highly rated carrier partners. Today, SageSure manages more than $3\.2 billion of inforce premium and helps protect more than 970,000 policyholders.
We have more than 1,000 employees in a distributed workforce environment across 12 offices—Fairfield, CA; Mountain View, CA; Cheshire, CT; Jacksonville, FL; Tallahassee, FL; Tampa, FL; Chicago, IL; Jersey City, NJ; Marlton, NJ; Cincinnati, OH; Houston, TX; Sheboygan, WI—who are tackling the industry's toughest challenges.
SageSure is a proud Equal Opportunity Employer committed to building a workforce that reflects the spectrum of perspectives, experiences, and abilities of the world we live in. We recognize that our differences make us strong, and we actively seek out diverse candidates through partnerships with organizations, institutions and communities that represent various backgrounds. We champion belonging and inclusion for all identities, including, but not limited to, race, ethnicity, religion, sexual orientation, age, veteran status, ability status, gender, and country of origin, striving to create a culture where all individuals feel valued, respected, and empowered to bring their authentic selves to work.
Our nimble, highly responsive culture nurtures critical thinkers who run toward problems and engineer solutions. We relentlessly pursue better outcomes by investing in the technology, talent, and tools that position us to succeed in demanding markets. Come join our team! Visit sagesure.com/careers to find a position for you.
California Applicants: View SageSure's Workforce Members' Privacy Notice \- CA Privacy Policy \& Notice of Collection
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 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At SageSure, 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 789 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
SageSure AI Hiring
SageSure has 1 open AI role right now. They're hiring across Data Scientist. Based in Fairfield, CA, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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