Sr Data Scientist ; Hybrid

Would you like to apply advanced actuarial science and machine learning to build predictive models that directly influence underwriting, pricing, and risk decisions for insurers at scale?

Lexis Nexis Risk Solutions is the essential partner in the assessment of risk. Within Insurance, we provide customers with solutions and decision tools that combine public and industry specific content with advanced technology and analytics to assist them in evaluating and predicting risk and enhancing operational efficiency. Our insurance risk solutions help drive better data‑driven decisions across the insurance policy lifecycle – all while reducing risk.

The Insurance Analytics team are the trusted leaders in analytics excellence, delivering innovative, data‑driven solutions through cutting‑edge data science and strategic risk solutions to drive market leadership, impactful change, and lasting value for our customers and stakeholders. The team is responsible for new product innovation, model development, and creating actionable insights for our customers. We work closely with the Vertical and Product teams to design and implement new solutions for the insurance and OEM markets.

By harnessing the power of data, our analytics team empowers insurers to make informed decisions, optimize risk segmentation, and enhance underwriting strategies, ultimately driving success in an ever‑evolving insurance landscape.

We are seeking a Senior Data Scientist I to join our Insurance Analytics team, with a strong foundation in actuarial science, statistical modeling, and data science. In this role, you will contribute to the development of innovative insurance products, advanced predictive models, and data‑driven insights that inform key business decisions. You will partner closely with Product and Vertical teams to design and deliver analytical solutions that address complex insurance challenges and support evolving market needs.

This role is ideal for someone who combines deep actuarial expertise with strong modeling intuition, is comfortable working with complex datasets, and can effectively translate analytical findings into clear, actionable recommendations that drive business and product outcomes.

• Develop and enhance statistical and machine learning models using structured and unstructured data to generate predictive insights and attributes.
• Design and build data pipelines and analytical solutions that support risk segmentation and insurance use cases.
• Provide actuarial expertise and recommendations to inform model development, risk segmentation, and support rate filings.
• Research and apply innovative data science methodologies to solve complex business problems.
• Manage and analyze large, complex datasets, including data storage, processing, and quality assurance.
• Apply best practices for data validation, testing, and model performance monitoring.
• Collaborate with team members to share knowledge, strengthen capabilities, and contribute to a strong analytical culture.
• Identify and leverage new data sources to improve and validate existing models.
• Partner with internal stakeholders to understand business needs, troubleshoot challenges, and deliver actionable insights.
• Maintain a strong understanding of team tools, technologies, and evolving industry trends.
• Apply business and domain knowledge to drive effective, practical solutions.
• Communicate progress, insights, and outcomes clearly to stakeholders.
• Support team excellence by upholding high standards of quality, accountability, and execution.

• Minimum undergraduate degree in relevant field and 4+ years of relevant work experience.
• Or a master’s degree in a relevant field and 2+ years of relevant work experience.
• Or a PhD in a relevant field.
• Strong expertise in Python. Coding skills in R, SQL, and ECL are a plus.
• Good foundation in actuarial science, including experience applying actuarial principles to pricing, risk segmentation, or model development.
• Strong foundation in statistical and mathematical modeling, including model assumptions, diagnostics, and interpretability. This includes linear and non‑linear…