• 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.

• Architect and deliver enterprise AI/ML solutions • Lead strategic initiatives and mentorship • Collaborate with product and engineering teams

• Work collaboratively with Human Centered Design lead to take input from multiple forums, analyze the data, visualize the data, and form insights and predictions supporting the Centers for Medicare & Medicaid Services (CMS) initiatives • Support the product design process with data in a way that reflects and responds to the functional, social, and emotional behavior of users • Build predictive models, data visualizations, and tools for analysis • Build AI tools that automate processes, recommendation engines, and automated lead scoring systems • Provide insights through the combination of qualitative design research and exploratory data analysis • Research and analyze data sources for patterns and clusters for CMS Procedure Price Lookup tool • Create data visualizations that tell stories to help stakeholders understand a problem space or lead to insight • Ability to solve problems while considering timeliness, effectiveness, and practicality • Ability to apply critical thinking skills to conduct an objective analysis • Programming skills in R, Python and/or other programming languages used for data science purposes • Work in an agile environment, delivering incremental product functionality in short iterations • Selecting features, building and optimizing classifiers using machine learning techniques • Data mining using state-of-the-art methods • Enhancing data collection procedures to include relevant information for analytic systems • Processing, cleansing, and verifying the integrity of data used for analysis • Performing ad-hoc analysis and presenting results clearly • Creating an automated anomaly detection system