About the job

- We are looking for an Associate Applied Scientist to join a dynamic and innovative AI platform team. If you are passionate about applying machine learning to knowledge graphs and reasoning systems at scale, this is an opportunity to build core components of Outreachs per-tenant knowledge graph while developing deep expertise under the guidance of senior scientists.

- 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 innovative, scalable, and reliable AI capabilities with direct impact on revenue outcomes.

- This role is ideal for someone with strong ML fundamentals who wants to build deep expertise in knowledge graphs and applied NLP in a fast-moving product environment.

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), including 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 Learning: 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 relationships under the guidance of senior scientists. Contribute to ontology design and documentation.

- Cross-functional Collaboration: Work with software engineers to deploy models and pipelines into production. Write clean, tested code. Monitor system health and respond to incidents. Participate in code review and design discussions.

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 relation ship extraction, graph neural networks, recommendation systems, or conversation AI and dialogue systems. MS + 2 years of relevant experiecne will also be considered.

- Solid engineering fundamentals. You can write production-quality code, not just prototype notebooks. You can write clean, tested Python code. Experience with graph databases or query languages (e. , Neo4j, SPARQL,



Cypher).

- Demonstrated ability to build and evaluate ML models. Youve trained models, measured performance using appropriate metrics, and iterated on results.

- 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.

- Robust Ownership: Take end-to-end responsibility for research and model development initiatives, from problem formul ation and data analysis through experimentation, production deployment, and ongoing performance monitoring, driving outcomes with minimal oversight.

- Good communication skills. You can explain technical concepts to engineers and product managers.

- Eager to learn. You are excited to develop deep expertise in knowledge graphs and applied NLP under the mentorship of senior scientists.

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

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.

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