Responsibilities
- Create and deploy AI-driven sub-agents that operate across customer lifecycle stages by integrating predictive modeling, contextual retrieval, and large language model reasoning to suggest or execute actions
- Develop predictive and prescriptive models focused on customer churn risk, growth patterns, product adoption trends, account health scoring, and related lifecycle challenges
- Construct the underlying data infrastructure and knowledge systems that support sub-agent decision-making, ensuring responsible data aggregation and privacy-conscious design
- Design retrieval mechanisms, grounding techniques, and decision rules for sub-agents, determining when to act autonomously, recommend, defer, or escalate
- Build evaluation frameworks to assess sub-agent performance and determine readiness for release, including monitoring for regressions in live environments
- Establish success metrics and experimentation approaches for sub-agent deployment, emphasizing measurable customer impact over theoretical performance
- Collaborate with Product, Engineering, and Applied AI teams from initial problem definition through to production implementation
- Foster data-driven practices within technical teams and provide mentorship to fellow data scientists
Work Arrangement
Remote (Country) — Bellevue, WA, US
