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The Principal AI/ML Scientist & Engineer will lead a team, focusing on AI/ML strategy and developing data products for business outcomes.

Lead a team of Applied Scientists and AI/ML Engineers, drive AI/ML strategy, optimize AI systems, and deliver production-ready solutions across the organization.

Lead and mentor a team of AI/ML Engineers, developing production-grade AI solutions that extract value from structured and unstructured data, with a focus on customer-centric performance.

Lead and mentor a team of Applied Scientists and AI/ML Engineers to design, build, and deploy production-grade AI features for an AI-native CRM. Drive technical strategy for LLM operations, agentic workflows, RAG/vector-based solutions, and scalable data pipelines while partnering with platform teams to ensure performant, customer-centric ML systems.

Lead and mentor a team of Applied Scientists and AI/ML Engineers to design and deliver production-grade AI solutions across structured CRM and unstructured data. Drive technical strategy for agentic workflows, RAG/vector DB systems, and real-time reasoning. Partner with AI Platform on LLMOps and cloud infrastructure, act as a technical authority, and contribute as a high-impact individual contributor to ship customer-focused AI products.

Lead marketing analytics and measurement: design experiments and incrementality tests, evolve MMM, build LTV/churn models, govern marketing data (dbt/SQL), partner cross-functionally to translate insights into executive recommendations and influence data stack and AI adoption.

Lead marketing analytics by building experimentation frameworks, MMM, LTV and churn models, and scalable data models (dbt/SQL). Translate insights for leadership, partner across Marketing, Finance and Data Engineering, and drive AI-augmented analytics and measurement frameworks to optimize budget allocation and customer outcomes.

Lead marketing analytics by building experimentation frameworks, MMM and incrementality testing, LTV and churn models, and scalable data models (dbt/SQL). Partner cross-functionally to translate insights into budget and product decisions, shape marketing data infrastructure, and apply AI to automate analytics.

Lead marketing analytics strategy by building experimentation frameworks, MMM, LTV/churn models, and scalable data models. Drive cross-functional initiatives, ensure data quality and tracking, translate findings into executive recommendations, and mentor analysts to improve marketing efficiency and unit economics.