Senior Actuary & Data Science Engineer

Job description

Arbital Health is a rapidly growing healthcare technology and actuarial leader that centralizes, measures, and adjudicates value-based care contracts at scale. We enable payers and providers to design, measure, and execute value-based agreements with greater transparency, efficiency, and financial predictability. We invest in hiring high potential and humble individuals who thrive in fast-paced environments and can rapidly grow their responsibilities as we continue to accelerate our growth. We were co-founded by Brian Overstreet and Travis May (founder & former CEO of LiveRamp and Datavant, the two biggest data companies of the last 20 years), and are backed by Transformation Capital, Valtruis and other leading investors. In our first 2 years, Arbital Health has established itself as a trusted partner for over 40 payers, providers, and other stakeholders looking to navigate the complexities of risk-based contracting. **The Opportunity** We are seeking an experienced, credentialed actuary (ASA or FSA) with a passion for software and modern technology to join us as Senior Actuary & Data Science Engineer. Unlike traditional actuarial roles, this position sits directly within our Technology Organization. You will serve as the bridge between complex actuarial science and production software—building scalable actuarial models, integrating them within our AI architecture, embedding actuarial professionalism into automated tools, and creatively solving traditional healthcare risk problems using novel and emerging practices. If you are an actuary who wants to build models at scale to increase their impact and to revolutionize value-based care and the actuarial practice through technology, this role is built for you. ### What you'll do: ● Production Model Engineering: Design, build, and deploy scalable actuarial models (e.g., IBNR, financial forecasting, risk adjustment, pricing, etc.) that plug directly into our platform APIs and core engineering pipelines to solve real and complex actuarial problems. ● AI Context & Efficacy: Collaborate with Product, Delivery, and other Engineering teams to inform domain context for our Arbital AI suite—refining prompts, expanding evaluation frameworks (e.g., CADRE), providing Golden question/answer pairs, and ensuring high fidelity in automated actuarial outputs. ● Actuarial Governance & Standards: Embed rigorous actuarial best practices, professionalism, and validation standards directly into our automated modeling tools and data pipelines. ● Domain-Driven Prototyping: Lead rapid proof-of-concept (POC) builds to de-risk complex actuarial logic and test new algorithmic approaches before working with other engineering teams to apply technical scaling. ● Cross-Functional Technical Leadership: Partner closely with Product, Engineering, and Delivery teams to translate messy real-world healthcare data problems into clean, scalable software solutions. ### What you bring: ● Credentials: Associate of the Society of Actuaries (ASA) required; Fellow of the Society of Actuaries (FSA) preferred. ● Experience: 6+ years of healthcare actuarial experience working with data (e.g., eligibility and claims) and building models. ● Technical Proficiency: Strong hands-on coding skills in Python, R, and SQL, with a demonstrated history of writing clean and reproducible code. ● Engineering Mindset: Experience with Git/GitHub for version control and collaborative development. ● Modern Tech Curiosity: Deep interest or experience in leveraging modern technology—such as generative AI, LLMs, PySpark, or cloud data infrastructure—to solve complex actuarial or data science problems. ● Problem Solver: Entrepreneurial mindset with the ability to thrive in a fast-paced technology organization and communicate complex actuarial concepts to other teams. ### Bonus points for: ● Hands-on experience with PySpark, Databricks, or distributed computing frameworks. ● Familiarity with modern AI architectures (e.g., RAG or OKF frameworks, model evaluation suites). ● Experience translating traditional actuarial logic into modern engineering languages.