• Develop automated reports, dashboards, and insights across the full credit lifecycle. • Support underwriting, risk management, portfolio monitoring, and credit performance. • Build credit risk dashboards, reports, and analytics using Python and Tableau. • Analyze the credit lifecycle (underwriting, performance, delinquency, and collections). • Develop and automate reporting pipelines and monitoring frameworks. • Translate analytics into strategies for risk, business, and client teams. • Perform deep‑dive analyses on credit behavior, defaults, and scoring models. • Present insights to clients in clear formats. • Guide clients in using dashboards and analytics for decision‑making. • Ensure data quality, accuracy, and compliance with reporting standards. • Improve existing reporting and build new analytics for better forecasting and risk assessment. • Mentor junior team members on visualization, reporting, and credit analytics. • Be a trusted advisor in client meetings, presentations, and workshops.

• Build and iterate on LLM & AI-powered product features • Design, prototype, and ship features that use LLMs (e.g., content generation, summarization, classification, semantic search, assistants, recommendations). • Work with engineers to integrate LLMs into the product via APIs or internal services (RAG, tools/functions calling, workflows, pipelines). • Define evaluation strategies for LLM features (e.g., human-in-the-loop evaluation, rubrics, prompt experiments, offline/online metrics). • Continuously refine prompts, data pipelines, and system design based on user behavior, quality metrics, and product goals. • Own product analytics for data- & AI-powered features • Partner with product managers and designers to define success metrics (e.g., adoption, engagement, conversion, retention, quality, time-to-value). • Instrument new features: define events, ensure proper logging, and validate that data is correct and trustworthy. • Analyze funnels, cohorts, user journeys, and experiment results to understand drivers of behavior and outcomes. • Translate insights into clear recommendations that influence roadmaps, prioritization, and feature iteration. • Work with real-world transactional data (SQL & NoSQL) Explore, clean, and transform data from transactional (OLTP), analytical (OLAP), and event-based systems. • Work across SQL (e.g., Postgres, Snowflake) and NoSQL (e.g., Redis, document/Key-Value stores). • Design data assets and features that are usable by both analytics workflows and LLM/ML systems. • Define and track data quality metrics (completeness, consistency, timeliness, drift, schema changes). • Build checks, monitors, and alerts to detect data issues that can affect analytics or AI/LLM performance. • Work with data and engineering teams to diagnose root causes and drive changes that improve data quality over time. • Use core ML concepts (feature design, model evaluation, bias/variance, generalization) to reason about LLM and non-LLM approaches. • When appropriate, build and evaluate lighter-weight or traditional models (e.g., scoring, ranking, classification) to complement or replace LLM solutions.

• 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.