Staff Applied Scientist - Knowledge Graphs & AI

About Outreach Outreach, founded in 2014, is the only complete agentic AI platform for revenue teams. Outreach infuses agentic AI, conversation intelligence, and assistive AI to power hundreds of use cases across revenue motions. From new logo prospecting to expansions, deal acceleration, driving retention, and forecasting, Outreach AI automates workflows and frees sellers to focus on more strategic conversations and actions. Revenue leaders benefit from connected account visibility, performance insights, and higher forecasting accuracy across every GTM team. World leading enterprise organizations use Outreach to power their revenue teams, including Databricks, SAP, Siemens, and Verizon to name a few. About the Team: Data is at the core of Outreach's strategy. It drives us and our customers to the highest levels of success. We use it for everything from customer health scores and revenue dashboards to operational metrics of our AWS infrastructure, to helping increase product engagement and user productivity through natural language understanding, to predictive analytics and causal inference via experimentation. As our customer base continues to grow, we are looking towards new ways of leveraging our data to deeper understand our customers’ needs and deliver new products and features to help continuously improve their customer engagement workflows. The mission of the Data Science team is to enable such continuous optimization by reconstructing customer engagement workflows from data, developing metrics to measure the success and efficiency of these workflows, and providing tools to support the optimization of these workflows. As a member of the team, you will work closely with other data scientists, machine learning engineers, and application engineers to define and implement our strategy for delivering this mission.

Your Daily Adventures Will Include:: Key Responsibilities: Knowledge Graph Design & Construction: Architect and evolve per-tenant knowledge graph schemas, including entity resolution, temporal modeling, and ontology design tailored to sales execution domains. Information Extraction: Architect NLP pipelines that extract structured knowledge from unstructured conversational and document data (sales calls, emails, CRM notes), including coreference resolution, relation extraction, and event detection. Contextual Reasoning & Recommendation: Design reasoning and inference layers over the knowledge graph to power next-best-action suggestions, deal risk scoring, coaching recommendations, and competitive intelligence surfaces. Representation Learning: Design and train graph-based models (GNNs, relational embeddings, link prediction) over heterogeneous, multi-relational graph structures to support downstream reasoning and retrieval tasks. Diagnose and address embedding quality issues including cold-start entities, and temporal drift. Domain Modeling: Formalize sales execution concepts such as deal stages, buyer engagement patterns, rep behaviors, and account health, into structured representations that ground the platform's AI capabilities. Extract ontology structure. Lead ontology versioning and migration. Cross-functional Collaboration: Partner with engineering, product, and data teams to bring models from prototype to production, ensuring reliability and measurable impact at scale. Our Vision of You:: Qualifications: PhD in a relevant field such as Computer Science, NLP, Machine Learning, or a related discipline with a focus on knowledge representation and reasoning, information extraction and relationship extraction, graph neural networks, recommendation systems, or conversation AI and dialogue systems. Strong engineering fundamentals. You can write production-quality code, not just prototype notebooks. Proficiency in Python; and graph databases or query languages (e.g.,