Role Description Data Scientist – Property & Casualty (P&C) - Drive actionable insights and inform corporate strategies through the use of advanced modeling, data mining, analytics, and market intelligence. - Demonstrate strong communication skills with a basic understanding of project management principles. - Ability to visualize data effectively on both simple and complex levels. - Proficiency in leveraging data, machine learning techniques, and statistical model frameworks to identify and create business opportunities. - Ability to work with data using query languages such as SQL, R, or Python, and understanding of relational data. - Demonstrate the ability to write production-ready code in Python or R. Knowledge of generative AI for coding is a plus. - Lead key countrywide product initiatives and partner with Actuarial, Underwriting, Distribution and Legal for execution. - Experienced in developing product proposals that appropriately balance profit and growth objectives. - Design, maintain and update exposure management tools and reports to support ongoing business decisions and regulatory requirements. Qualifications - Master’s degree in Data Science or related field (e.g., Statistics, Applied Economics) required. Requirements - 5+ years of experience in P&C insurance product management, pricing, or analytics. - Experience in Personal Lines insurance. - Familiarity with cloud-based analytics platforms and modern data engineering practices. - Experience supporting product strategy, pricing, exposure management, or regulatory reporting. - Strong programming skills in Python, R, and SQL. - Experience developing predictive models, machine learning solutions, and statistical analyses. - Experience creating dashboards and visualizations using business intelligence tools. - Strong analytical, communication, and project management skills. - Experience with generative AI tools for software development or analytics is a plus. Benefits - Salary Range: $89,800.00 - $125,500.00 - Salary is commensurate to experience, location, etc. Company Description Horace Mann was founded in 1945 by two Springfield, Illinois, teachers who saw a need for quality, affordable auto insurance for teachers. Since then, we’ve broadened our mission to helping all educators protect what they have today and prepare for a successful tomorrow. We serve more than 4,100 school districts nationwide, we’re publicly traded on the New York Stock Exchange (symbol: HMN) and we have more than $12 billion in assets. We’re motivated by the fact that educators take care of our children’s future, and we believe they deserve someone to look after theirs. We help educators identify their financial goals and develop plans to achieve them. This includes insurance to protect what they have today and financial products to help them prepare for their future. Our tailored offerings include special rates and benefits for educators. - EOE/Minorities/Females/Veterans/Disabled. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability or protected veteran status. - For applicants that are California residents, please review our California Consumer Privacy Notice. - All applicants should review our Horace Mann Privacy Policy.

What You Will Do💡 - Marketplace Modelling: Build and maintain ML and optimisation models across the quick-commerce stack — supply-demand matching, dynamic and surge pricing, recommendations, ETA prediction, and broader marketplace optimisation. - Butler & Conversational AI: Contribute to the AI behind Butler, Mrsool's distinctive conversational ordering experience — modelling customer intent from free-form, unstructured requests (text, voice, images) and mapping it to fulfillable, well-priced orders. - Experimentation & Causal Inference: Design and run experiments (A/B and quasi-experimental) across pricing, matching, recommendations, and Butler, and turn noisy marketplace data into decisions stakeholders can act on. - Feature Engineering & Data Craft: Engineer high-signal features from messy, real-world data — order events, courier traces, geospatial signals, pricing configs, and conversational text/voice — as a core, ongoing part of the role. - Production ML: Own your models through their lifecycle — data pipelines, training, deployment, monitoring, and retraining — and respond when a model or config drifts. - Cross-Functional Collaboration: Collaborate effectively with product managers, engineers, DevOps, operations, and other squads to deliver seamless, data-driven experiences and to help diagnose live issues (e.g. mispriced brackets, elevated failure rates in a city). - Operational Excellence: Proactively monitor model and metric health, instrument your work with proper logging and observability, and contribute to reliable, repeatable analysis and deployment practices. - Continuous Improvement: Identify opportunities to improve measurement, modelling, and process; favour small, incremental changes that compound over time.

• Own and develop the product analytics function • Work closely with Product, Engineering, and Leadership teams • Proactively identify what should be analyzed, validated, or challenged — without waiting for requests from Product or Leadership • Run deep product analysis to identify opportunities for growth and optimization • Drive product decisions with data, not just respond to ad-hoc requests • Design event tracking from scratch and maintain scalable, reliable analytics instrumentation • Own data quality and metric consistency across the product • Improve and optimize the DWH layer, including data modeling and performance optimization • Build and maintain dashboards and analytical layers • Develop product metrics, frameworks, and measurement approaches • Help teams make faster and better product decisions through data

• Design and maintain the core AI orchestration layers, centralized API gateways, and reusable frameworks (e.g., advanced RAG architectures, agentic frameworks) for company-wide consumption. • Collaborate directly with core engineering teams to embed production-ready generative AI and machine learning features into the Actian Data Intelligence Platform. • Establish strict guardrails, evaluation frameworks, and monitoring tools to track model performance, bias, data privacy, and security across all AI implementations. • Actively monitor and manage cloud and API compute spend (token management, open-source vs. commercial models) and optimize execution latency for production AI features. • Lead workshops, design blueprints, and create documentation to empower non-AI engineering teams to build and maintain their own AI-driven features confidently. • Drive the engineering execution of high-impact AI proof-of-concepts, ensuring they are built with production-grade code that scales seamlessly. • Define and enforce the organization's official AI stack, from vector database selection and vector embeddings strategies to semantic caching mechanisms. • Evaluate and manage partnerships with AI model providers and lead the technical assessment of cutting-edge open-source models to keep Actian at the vanguard of innovation. • Define and track operational metrics for the AI Enablement function, such as developer adoption rates, reduction in time-to-market for AI features, and ROI of implemented AI tools.