Sr Data Scientist I (Actuarial Science)

Would you like to apply actuarial science and statistical modeling to build predictive models that directly influence underwriting, pricing, and risk decisions for insurers at scale?  About the Business     LexisNexis 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. You can learn more about LexisNexis Risk at the link below.   https://risk.lexisnexis.com/insurance    About our Team     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.    About the Role     Are you an actuarial professional who wants to build models that influence underwriting, risk segmentation, and decision-making across the insurance industry, without being confined to traditional rate-making roles?  We are seeking a  Senior Data Scientist I  with a strong actuarial foundation to join our Insurance Analytics team, focused on the  commercial insurance market . In this role, you will design and develop predictive models that are embedded in carrier workflows and used to inform underwriting decisions, segmentation strategies, and downstream pricing models.  Unlike traditional actuarial roles, you will focus on building  external-facing risk models and attributes  that insurers integrate into their pricing and underwriting frameworks. This is an ideal opportunity for someone with actuarial training who enjoys applying statistical modeling and analytical thinking to broader insurance problems at scale.  Responsibilities:     Developing predictive risk models and attributes  used by insurers in underwriting, segmentation, and decisioning workflows  Applying  actuarial principles and statistical modeling techniques  to assess risk and improve model performance  Designing and implementing models that are integrated into  carrier underwriting processes and downstream pricing frameworks   Translating complex analytical outputs into  clear, defensible insights for business and product stakeholders   Partner with Product and Vertical teams to solve  insurance-specific problems related to risk evaluation and segmentation   Managing and analyzing large, complex datasets, including data storage, processing, and quality assurance.   Applying best practices for data validation, testing, and model performance monitoring.   Collaborating with team members to share knowledge, strengthen capabilities, and contribute to a strong analytical culture.   Maintaining a strong understanding of team tools, technologies, and evolving industry trends.   Communicating progress, insights, and outcomes clearly to stakeholders.   Supporting team excellence by upholding high standards of quality, accountability, and execution.   Requirements:     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 actuarial foundation , including experience applying actuarial concepts to insurance risk, underwriting, or segmentation problems  Progress toward  actuarial credentials (ASA or equivalent)  strongly preferred  Strong expertise in Python.  Coding skills in R, SQL, ECL are a plus.   Experience developing or supporting  risk segmentation models  (e.g., GLMs) in an insurance context and in Department of Insurance filings.  Experience translating actuarial models into production-ready analytical solutions.  Strong foundation in statistical and mathematical modeling, including model assumptions, diagnostics, and interpretability.   This includes linear and non linear models along with ML techniques.  Extensive programming skills in Python and/or R for statistical modeling and data analysis  Strong ability as a self-starter to learn new technologies and to share cross-functional knowledge across the teams nice to have.   Technical/Professional Experience    Able to build or test new processes with senior guidance. Domain expert in Data Science, Actuarial Science and/or Statistical Analysis to build advanced models and roll into production.    Scopes and execute analytical approaches for moderately complex problems, seeking input where needed.   Supports, maintains, and enhances existing models (e.g., GLM and tree-based methods).   Applies statistical, mathematical, predictive modeling and analytical techniques to work with large, complex datasets from diverse sources.   Data Skills     Independently prepares, cleans, and transforms data for analysis and modeling.   Applies a range of data processing techniques and explores new methods to improve data quality and usability.     Project Management Skills     Owns and delivers components of projects independently, including planning and execution of key tasks.   Contributes to larger, more complex projects by executing defined workstreams and meeting timelines.   Domain/Industry Skills     Experience working