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., Neo4j, SPARQL, Cypher) is required. • Comfort with ambiguity. You can take a vague product goal and decompose it into concrete technical problems. You don't need a fully scoped spec to start making progress. • A track record of building things: whether that's research prototypes that went beyond the paper, open-source contributions, or side projects that required real systems thinking. You understand the gap between a research prototype and a reliable production system, such as monitoring, data drift, latency, and operational excellence. • Strong Ownership: Take end-to-end responsibility for research and model development initiatives, from problem formulation and data analysis through experimentation, production deployment, and ongoing performance monitoring, driving outcomes with minimal oversight. • Strong communication skills with the ability to translate research concepts into product impact for cross-functional audiences. • Experience mentoring or leading technical work. You've helped junior team members grow and have driven cross-team technical decisions. Nice to Have: • 2+ years of hands-on experience applying knowledge graphs or graph-based learning methods to real-world data in a production setting. • Strong fundamentals in at least two of: knowledge graph construction, information extraction, graph neural networks, or recommender systems. • Experience working with large-scale unstructured text data (conversational transcripts, email, or similar) • Experience with probabilistic graphical models, conversational AI, or sales/revenue domain data • Published research at top-tier venues Why Join Us? • Greenfield Architecture: Shape the design of a core AI system from the ground up, with the latitude to make foundational technical decisions that define the platform. • Depth That Matters: This role genuinely requires PhD-level thinking; you will tackle problems in entity resolution, temporal reasoning, and graph learning that demand it. • Applied Impact: Work with real production feedback loops and millions of sales interactions, not just benchmarks; see your models change how thousands of teams sel