Director of Applied Science and Engineering - Knowledge Graphs & AI

Technical Vision & Strategy: Define and own the multi-year technical roadmap for Outreach's Knowledge Graph platform, including entity resolution, temporal reasoning, graph-based learning, and contextual inference. Translate business objectives into a coherent applied science strategy that balances research ambition with production delivery.

Team Leadership: Build, hire, and lead a team of applied scientists and research engineers. Establish team culture, research rigor, career development frameworks, and a high bar for both scientific quality and production impact. Mentor senior ICs into technical leaders.

Knowledge Graph Architecture: Drive the design of per-tenant knowledge graph schemas, ontologies, and data models tailored to the sales execution domain. Own decisions on graph databases, query languages, storage engines, and tenant isolation strategies at scale.

Information Extraction at Scale: Oversee pipelines that extract structured knowledge from unstructured conversational and document data (sales calls, emails, CRM notes), including coreference resolution, relation extraction, event detection, and entity linking.

Reasoning & Inference Systems: Lead the development of reasoning and inference layers over the knowledge graph to power next-best-action suggestions, deal risk scoring, coaching recommendations, competitive intelligence, and agentic AI decision-making.

Representation Learning & Graph ML: Direct research into graph-based models (GNNs, relational embeddings, link prediction, temporal graph networks) over heterogeneous, multi-relational graph structures to support downstream reasoning, retrieval, and recommendation tasks.

Cross-functional Leadership: Partner with leaders in Engineering, Product, Design, and Data to align science investments with product priorities. Represent the applied science function in executive reviews, roadmap planning, and technical design reviews.

Research-to-Production Pipeline: Establish processes and infrastructure for moving from research exploration to production deployment: experiment tracking, model evaluation frameworks, A/B testing, and continuous model improvement loops.

Industry & Academic Engagement: Keep the team at the frontier of knowledge graph research. Foster connections with the academic community through conference participation, publications, and strategic academic partnerships.