• Partner with Clinical, Medical, and Operations teams to apply data science and AI to longevity-focused care delivery, including hormone optimization, metabolic health, cardiovascular risk, and preventative care pathways. • Develop and evaluate models and AI-driven workflows that support clinicians in delivering personalized, evidence-based longevity care at scale. • Design and implement agentic AI workflows to assist with tasks such as longitudinal chart review, lab trend interpretation, clinical summarization, follow-up planning, and patient outreach. • Analyze longitudinal patient data; including labs, vitals, prescriptions, and engagement signals; to identify opportunities to improve outcomes, adherence, and care consistency over time. • Build analytical frameworks to measure clinical effectiveness, physician productivity, and patient safety in a virtual care environment. • Collaborate with product and engineering teams to integrate AI-powered tools directly into telehealth clinical workflows, ensuring high usability and clinician trust. • Define and track key performance indicators related to longevity outcomes (e.g., biomarker improvement, therapy adherence, patient engagement) and operational efficiency (e.g., time per visit, panel capacity). • Evaluate and monitor AI-assisted clinical tools with a focus on reliability, bias, and real-world performance, incorporating clinician feedback and human-in-the-loop controls. • Communicate insights, tradeoffs, and recommendations clearly to clinical leaders and cross-functional stakeholders. • Contribute to shared standards and best practices for responsible, compliant AI use in longevity telehealth.

• Partner with Marketing teams to analyze and optimize the full customer funnel, including acquisition, conversion, retention, and churn. • Build and maintain statistical models and analyses related to key marketing use cases such as attribution, LTV, CAC, churn prediction, cohort analysis, and conversion optimization. • Design, analyze, and interpret experiments (A/B and multivariate tests) across marketing channels and on-site experiences, ensuring statistical rigor and clear recommendations. • Translate ambiguous business questions into well-scoped analytical projects with clear success metrics and timelines. • Collaborate with data engineering and analytics engineering partners to define data requirements and ensure reliable, well-modeled datasets for marketing analytics. • Clearly communicate findings and recommendations to non-technical stakeholders through presentations, dashboards, and written documentation. • Contribute to shared analytics and modeling best practices within the data science team, including code quality, validation, and documentation. • Support junior team members through informal mentorship, code review, and knowledge sharing where appropriate. • Stay current on industry trends and techniques in marketing analytics, experimentation, and applied data science, and apply them pragmatically to Hone’s context.

• Participate in clinical projects with external Pharma, academic and other partners • Represent the Outcomes Research function and collaborate with internal and external stakeholders in the design, analysis, interpretation and publication of clinical real world studies • Work on complex problems, exercising judgment in selecting and adapting methods as appropriate • Work with interdisciplinary groups of scientists, engineers, and product developers to translate research into clinically actionable insights for our clients • Stay current with the latest methodological advances in real world studies • Build infrastructure including reusable code • Comply with all applicable regulations and Company procedures

- Design, develop, and deploy **time-series forecasting models** including ARIMA, SARIMA, and ETS - Build **machine-learning-based forecasting models** using GBM, Random Forest, XGBoost, and LightGBM - Develop **hierarchical and multi-level forecasting** solutions across products, regions, and channels - Perform large-scale data extraction, transformation, and analysis using **SQL** - Implement and operationalize models in **cloud environments** (Azure preferred) - Collaborate with business stakeholders to translate demand planning requirements into scalable analytics solutions