Senior Applied Scientist

About the Role

The Associate Applied Scientist role focuses on applying machine learning to knowledge graphs and reasoning systems at scale. This position involves building core components of Outreach's per-tenant knowledge graph and developing expertise under senior scientists. The team is developing a per-tenant contextual knowledge graph to capture customer sales environments and power contextual reasoning across the platform, driving recommendations and insights.

Key Responsibilities

• Knowledge Graph Design & Construction: Design and implement entity resolution and ontology population. Write and optimize queries for graph traversal and feature extraction. Own data quality for assigned domains.
• Information Extraction: Build pipelines to extract structured knowledge from unstructured conversational and document data. Conduct experiments to compare approaches and improve accuracy metrics.
• Contextual Reasoning & Recommendation: Implement graph traversal logic and feature queries for downstream scoring signals. Build and maintain features for deal risk, next-best-action, or coaching recommendation surfaces.
• Representation Learning: Train and evaluate link prediction and node classification models using graph embedding methods. Implement evaluation pipelines and track model performance.
• Domain Modeling: Translate sales concepts into graph nodes and relationships. Contribute to ontology design and documentation.
• Cross-functional Collaboration: Work with software engineers to deploy models and pipelines. Write clean, tested code. Monitor system health and respond to incidents. Participate in code review and design discussions.

Requirements & Tech Stack

• Python
• Graph databases or query languages (e.g., Neo4j, SPARQL, Cypher)

Qualifications

• PhD in a relevant field (e.g., Computer Science, NLP, Machine Learning) with a focus on knowledge representation and reasoning, information extraction and relationship extraction, graph neural networks, recommendation systems, or conversation AI and dialogue systems.
• An MS + 2 years of relevant experience will also be considered.
• Solid engineering fundamentals: Ability to write production-quality code.
• Demonstrated ability to build and evaluate ML models: Experience training models, measuring performance with appropriate metrics, and iterating on results.
• A track record of building things: Including research prototypes, open-source contributions, or side projects. Understanding the gap between research prototypes and production systems (monitoring, data drift, latency, operational excellence).
• Strong Ownership: End-to-end responsibility for research and model development initiatives.
• Good communication skills: Ability to explain technical concepts to engineers and product managers.
• Eagerness to learn: Excitement to develop deep expertise in knowledge graphs and applied NLP.

Nice to Have

• 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.