• Apply statistical analysis and experimentation to evaluate generative AI, agentic AI, and other AI-enabled applications • Develop and maintain Responsible AI measures, benchmark datasets, test suites, and evaluation criteria • Evaluate AI systems for fairness, bias, explainability, transparency, hallucination, safety, reliability, and robustness • Use evaluation results to support the design, testing, selection, and ongoing assessment of AI-enabled business applications • Establish performance baselines and help identify changes in model or application behavior over time • Design and execute proofs of concept for new Responsible AI evaluation approaches • Assess emerging Responsible AI tools, frameworks, and methodologies for validity, limitations, and business applicability • Support the integration of Responsible AI testing into application development, quality assurance, and governance processes • Partner with engineering, architecture, quality assurance, governance, and business teams to align evaluation methods with use case requirements • Create and maintain model cards, evaluation reports, methodology documentation, and related guidance • Communicate findings, limitations, risks, and recommendations to technical and nontechnical stakeholders • Contribute to Responsible AI training, internal education, applied research, and industry engagement • Monitor emerging technologies and evaluation methods related to generative AI, agentic AI, and Responsible AI.

• Lead the development of models and decision systems that improve how Honor matches caregivers and clients, constructs sustainable schedules, allocates capacity, prioritizes operational work, and responds to changing conditions. • Your team will work directly with Product, Engineering, and Care Operations to turn difficult operating problems into production systems that improve measurable outcomes. • Contribute to Honor’s broader Data Science and Applied AI direction. • Help shape the team’s roadmap and remain hands-on as a player-coach. • Partner with Product, Engineering, and Operations from problem definition through deployment, adoption, and iteration. • Establish strong standards for evaluating, monitoring, and responsibly deploying machine learning and AI-enabled systems. • Lead, coach, and grow the team while remaining hands-on in high-priority projects as a player-coach.

• Design and ship AI sub-agents that act across the customer lifecycle, combining predictive models, retrieved context, and LLM reasoning to recommend or take action • Build the predictive and prescriptive models that power those sub-agents churn risk, growth, adoption trajectories, account health scoring, and similar lifecycle problems • Develop the data foundations and knowledge layer those sub-agents reason over, applying responsible aggregation and privacy-aware design • Design the tools, retrieval, and grounding strategies each sub-agent uses; decide when a sub-agent should act, recommend, defer, or escalate • Build the evaluation harnesses that determine when a sub-agent is good enough to ship and that catch regressions in production • Define metrics and experimentation strategy for sub-agent rollouts; measure real customer impact, not just offline accuracy or eval scores • Partner with Product, Engineering, and Applied AI teams from problem framing through production deployment • Drive a data and modeling culture within Product and Engineering, and mentor other data scientists on the team

• Build and validate machine learning, statistical, and optimization models for business use cases. • Analyze large and complex datasets to identify actionable insights and recommend solutions. • Develop automated analytical workflows, reports, dashboards, and reusable data pipelines. • Assist Senior Data Scientists to evaluate model performance and document methodologies. • Present technical findings and business impact to technical and non-technical stakeholders. • Collaborate with Product, Engineering, and Analytics teams to translate business requirements into data science solutions. • Contribute to code reviews and knowledge sharing.