About the job:

- We are looking for an Applied Scientist to join a dynamic and innovative AI platform team that is pushing the boundaries of whats possible in sales execution. If you are passionate about applying cutting-edge research in knowledge graphs and reasoning systems to real-world problems at scale, this is an exceptional opportunity to shape a core piece of Outreachs AI architecture from the ground up.
- Our team is building a per-tenant contextual knowledge graph that captures the full complexity of each customers sales environment: accounts, deals, contacts, rep behaviors, competitive landscape, and the signals buried in calls, emails, and CRM activity. This graph powers contextual reasoning across the platform, driving next-best-action recommendations, deal risk signals, coaching suggestions, and competitive intelligence. In this pivotal role, you will design the underlying representations, extraction pipelines, and reasoning layers that make this possible, working closely with cross-functional engineering and product teams to deliver cutting-edge, scalable, and reliable AI capabilities with direct impact on revenue outcomes.

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 platforms 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 dont need a fully scoped spec to start making progress.
- A track record of building things: whether thats 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. Youve 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

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Staff Applied Scientist - Knowledge Graphs & AI (Hyderabad)
🏢 Outreach
📍 Hyderabad