- Help support Auto and Home Actuarial team update Base Rate Offset tool into Python. This role requires strong Python skills as well as self-learning skills. - Contributes to the development and implementation of predictive analytics through the application of adva nced statistical and analytical techniques in order to deliver data driven insights supporting business objectives. - Uses appropriate modeling techniques to address business needs. - Utilizes broad knowledge of advanced modeling techniques and procedures to develop new modeling techniques and skills. Conducts appropriate evaluation of model performance. - Designs and publishes reports to communicate results and track model performance. - Develops various programs including predictor and response variable programs. Reviews programs to ensure they conform to quality standards. - Supports preparation of internal/external, structured/unstructured data sets to build/rebuild/refresh predictive models. - Creates ad-hoc data analyses, as needed Communicates analytics to other modelers as well as to non-technical business partners. - Contributes to the continuous improvement of the modeling process. Contributes to the continuous improvement of the modeling process.

• The Senior Data Scientist is a strategic leader in our organization, driving the entire lifecycle of data science initiatives that directly impact healthcare outcomes. • Leverage your deep expertise and mastery of machine learning to develop, implement, and evaluate complex AI models in healthcare settings. • Mentor and develop junior data scientists and analysts, fostering a culture of data-driven innovation. • Lead and manage the entire lifecycle of data science projects, from conceptualization and design to development, deployment, and ongoing optimization. • Collaborate with cross-functional teams to define project scope, objectives, analytic design, validation strategy, and expected impact.

• Work on well-defined analytical tasks with guidance • Build strong foundations in data science and analytics • Deliver high-quality, reliable analytical outputs that support business decision-making • Contribute to the execution of data-driven initiatives by working on data preparation, analysis, and validation • Apply statistical and analytical techniques to solve business problems under guidance • Support the development of scalable and repeatable analytical processes • Collaborate with cross-functional teams to understand business context and align solutions with organizational goals • Communicate insights and findings through clear documentation and presentations

• Lead the design, development, deployment, and optimization of machine learning, predictive analytics, and AI-powered solutions. • Translate business challenges and opportunities into analytical approaches, model specifications, and measurable success criteria. • Apply advanced statistical analysis, machine learning techniques, and data science methodologies to solve complex business problems. • Analyze large, complex datasets to identify trends, patterns, opportunities, and actionable insights. • Develop and maintain model documentation, technical specifications, and implementation plans. • Stay current with emerging technologies, tools, and best practices in data science, machine learning, and artificial intelligence. • Design and execute comprehensive validation and evaluation strategies for machine learning and generative AI solutions. • Develop benchmarking frameworks and success metrics to assess model performance, reliability, and business impact. • Evaluate model quality using quantitative and qualitative measures, including accuracy, precision, recall, robustness, latency, and business outcome metrics. • Assess generative AI applications for response quality, grounding, relevance, consistency, and hallucination risk. • Identify and mitigate risks related to bias, fairness, explainability, privacy, and model reliability. • Perform model validation, testing, and performance assessments prior to production deployment. • Establish monitoring processes and evaluation methodologies to ensure continued model effectiveness and alignment with business objectives. • Design, execute, and analyze experiments, including A/B tests and statistical studies, to measure product and business outcomes. • Define key performance indicators and success metrics for machine learning and AI initiatives. • Measure and communicate the impact of analytical solutions through statistical analysis and quantitative methods. • Partner with stakeholders to define hypotheses, success criteria, and decision-making frameworks. • Use experimentation and data-driven insights to guide product, operational, and strategic decisions. • Collaborate with Engineering and Data Engineering teams to implement, operationalize, and scale models in production environments. • Monitor deployed models for performance degradation, model drift, data quality issues, and changing business conditions. • Recommend retraining, optimization, or replacement strategies based on model performance and evolving business needs. • Support the creation of scalable, maintainable, and reliable AI and machine learning solutions. • Ensure model deployment processes align with engineering best practices and operational requirements. • Partner with Product, Engineering, Analytics, and business stakeholders to prioritize opportunities and deliver high-impact solutions. • Communicate complex analytical findings and technical concepts to both technical and non-technical audiences. • Present recommendations, insights, and model performance results to leadership and project teams. • Support technical reviews, project planning, and delivery activities across cross-functional initiatives. • Contribute to knowledge sharing, documentation, and best practices within the data science organization. • Provide technical guidance and mentorship to junior team members and peers as needed.