Senior 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. Key Responsibilities: Knowledge Graph Design & Construction: Design and implement entity resolution and ontology population within established graph schemas. Write and optimize queries for graph traversal and feature extraction. Own data quality for assigned domains. Information Extraction: Build pipelines that extract structured knowledge from unstructured conversational and document data (sales calls, emails, CRM notes), in cluding coreference resolution, relation extraction, and event detection. Run experiments to compare approaches and improve accuracy metrics. Contextual Reasoning & Recommendation: Implement graph traversal logic and feature queries that feed downstream scoring signals. Build and maintain features for deal risk, next-best-action, or coaching recommendation surfaces. Representation L earning: Train and evaluate link prediction and node classification models using established graph embedding methods. Implement evaluation pipelines and track model performance over time. Domain Modeling: Translate sales concepts, such as deal stages, buyer engagement patterns, rep behaviors, and account health, into graph nodes and r