Data Scientist – Insurance & Actuarial Analytics Location: Fully remote in the United States Department: Data Science / Actuarial Analytics Type: Contract-to-hire About the Role We are seeking a highly analytical and technically proficient Data Scientist to join our client's growing insurance analytics team. This individual will play a key role in developing and maintaining predictive models that drive strategic decisions in underwriting, pricing, customer retention, and operational performance. The ideal candidate has a strong foundation in programming, statistical modeling, and machine learning, along with insurance or actuarial domain experience. You’ll work closely with actuarial, underwriting, and business stakeholders to build models and deliver actionable insights through storytelling, data synthesis, and visualization tools such as Power BI or Tableau. Key Responsibilities • Build and deploy predictive models to forecast loss ratios, customer retention, and quote-to-close rates. • Develop and implement machine learning and statistical models using Python, R, and SQL. • Perform data wrangling and preparation on structured and unstructured datasets. • Apply actuarial and insurance analytics techniques to support pricing, reserving, and product innovation. • Explore and implement time series forecasting and unsupervised learning algorithms for trend detection and segmentation. • Design and implement model monitoring pipelines to track performance over time and flag data drift or degradation. • Conduct inferential statistical analysis to support hypothesis testing and business decision-making. • Create compelling data visualizations and dashboards using Power BI (Tableau experience welcome). • Translate complex analytical findings into clear, actionable insights through data storytelling and stakeholder presentations. Requirements • Bachelor’s or Master’s degree in Data Science, Statistics, Actuarial Science, Computer Science, or related field. • 3+ years of experience in a data science or actuarial analytics role, ideally within insurance or financial services. • Proficient in Python, SQL, and R for data manipulation and modeling. • Strong experience in predictive modeling, including regression, classification, and ensemble methods. • Familiarity with loss modeling, customer retention analysis, and quote conversion models. • Demonstrated knowledge of time series, clustering, and dimensionality reduction techniques. • Experience with model validation, monitoring, and performance tracking. • Solid understanding of inferential statistics and experimental design. • Ability to synthesize findings into presentations and communicate results to both technical and non-technical stakeholders. • Strong skills in Power BI; experience with Tableau is a plus. Preferred Qualifications • Experience working in a regulated industry (e.g., P&C insurance, health insurance). • Familiarity with actuarial tools and frameworks (e.g., GLM, credibility theory). • Exposure to cloud-based environments (AWS, Azure, GCP) and version control (Git).