Senior Data Scientist - AI

Arrowhead Programs•San Diego, CA

About The Position

Built on meritocracy, our unique company culture rewards self-starters and those who are committed to doing what is best for our customers. Arrowhead Intermediaries is looking for a Senior Data Scientist to join our Data & Analytics Team! Join us as a Senior Data Scientist, with a strong focus on how AI and large language models can help a business win – not just how to run a better model. You are comfortable bridging the gap between cutting-edge technology and practical business constraints, asking questions that actuaries, underwriters, and business leaders haven’t thought to ask yet, and finding ways to answer them. You take ownership of outcomes and not just outputs. This is an opportunity to shape how a leading global insurance intermediary thinks about AI-driven underwriting that improves risk selection, enhances pricing models, and drives profitability across our property and casualty insurance products. You will use generative AI as a force multiplier – enabling you to independently prototype data pipelines, unlock alternative data sources, and deliver end-to-end solutions that would traditionally require multiple engineering handoffs. You are proactively identifying where AI can move the needle on underwriting profitability and risk selection. Most importantly, you are a team player. You value working in a collaborative environment, helping others succeed and contributing to shared outcomes with humility, curiosity, and clear communication.

Requirements

5+ years of experience in data science or applied machine learning, with demonstrated end-to-end model ownership from data collection through production deployment.
You can lead projects independently while keeping stakeholders aware of progress, risks, and limitations.
Hands-on experience with large language models and generative AI frameworks, including Microsoft Foundry and/or Azure OpenAI endpoints, as well as LLM orchestration tools such as LangChain, LangGraph, or equivalent R-based tools such as ellmer.
Proficiency in R and/or Python, with comfort working across both languages in a production environment.
Strong foundation in statistical modeling, including GLMs, gradient boosting, and other techniques used for risk assessment and pricing.
Experience building and deploying models in cloud-based production environments (Azure preferred).
Demonstrated ability to communicate complex technical findings clearly to business stakeholders (written, verbal, and data visualization).
A strong appreciation for contributing and maintaining well-written documentation.
You value version control (git).
Strong problem-solving orientation with a bias toward action and delivery.
Applicants must be authorized to work for any employer in the U.S. We are unable to sponsor or take over sponsorship of an employment Visa.

Nice To Haves

Experience in property and casualty insurance, specialty lines, or a related financial services domain with familiarity with underwriting workflows and pricing concepts.
Familiarity with actuarial methods, GLM-based pricing, or experience collaborating with actuarial teams.
Experience with Posit Connect, Posit Workbench, Shiny, or Quarto for production deployment and reporting.
Knowledge of web scraping, API integration, or unstructured data extraction pipelines.
Demonstrated ability to mentor data scientists and elevate team technical capabilities.
Experience setting up or contributing to MLOps practices, including model versioning, code reproducibility management, and CI/CD for analytical pipelines.

Responsibilities

Identify, extract, and integrate non-traditional data sources into underwriting and pricing workflows using generative AI, LLMs, and AI-assisted pipeline development
Prototype, build, and iterate on data enrichment pipelines rapidly, validating signal quality and relevance before integration into production models – expanding the team’s data capabilities without traditional engineering dependencies.
Design, build, and maintain advanced predictive models for risk frequency, severity, and loss ratio that directly inform point-of-sale underwriting decisions.
Collaborate closely with actuaries and fellow data scientists to ensure all models are actuarially sound, statistically defensible, and aligned with pricing objectives.
Own models end-to-end — from hypothesis formation and data collection through to production deployment and ongoing monitoring.
Apply the latest generative AI tools, agentic frameworks, and LLM capabilities to real underwriting and pricing problems — staying current on developments and evaluating new approaches proactively.
Ensure all solutions are maintainable, well-documented, and consistent with established team standards for reproducibility and deployment.
Partner directly with underwriters and business leaders to understand portfolio needs, translate business problems into analytical solutions, and communicate findings clearly to non-technical stakeholders.
Serve as a technical authority and mentor for the broader data science team — elevating the team's fluency in generative AI tooling and best practices.
Contribute to a collaborative, standards-driven team environment where your work can be maintained and built upon by others.