Staff Applied Scientist - Knowledge Graphs & AI

Ovii's Interpretation of the Role

The Staff Applied Scientist will design knowledge graph schemas, build graph‑based ML models, and create NLP pipelines that power next‑best‑action recommendations for Outreach's revenue platform. You will partner with product and engineering to ship research‑grade solutions at scale while mentoring junior engineers.

Role Snapshot

• Design and evolve per‑tenant knowledge graph schemas
• Develop graph‑based machine learning models
• Build NLP pipelines for information extraction
• Drive end‑to‑end research to production deployment
• Mentor junior engineers and lead technical decisions
• Collaborate cross‑functionally with product and engineering

Must-Have Requirements

• PhD in a relevant field
• Python
• Graph databases or query languages (Neo4j, SPARQL, Cypher)
• Production‑quality code
• PhD-level research in knowledge representation, reasoning, or graph ML
• PhD in Computer Science, NLP, Machine Learning, or related discipline
• PhD degree

Nice-to-Have Signals

• 2+ years applied knowledge‑graph experience
• Graph Neural Networks
• Large‑scale unstructured text processing
• Probabilistic graphical models
• Published research
• Conversational AI / dialogue systems
• Applied knowledge‑graph or graph‑based learning work
• Large‑scale text data processing

Work Setup

• Location: Hyderabad, India
• Work mode: REMOTE
• Remote scope: UNSPECIFIED
• Employment type: Full-Time

Eligibility Gates

• Visa sponsorship: unknown

Not Specified in JD

• Visa sponsorship
• Salary range
• Remote eligibility
• Travel
• Security clearance
• Coding test

What You'll Likely Work On

• Architect and maintain per‑tenant knowledge graph schemas, including entity resolution and temporal modeling
• Create NLP pipelines that turn conversational and document data into structured knowledge
• Build reasoning layers over the graph to generate next‑best‑action, risk scoring, and coaching recommendations
• Design and train graph neural network and relational embedding models for link prediction and retrieval
• Diagnose embedding quality issues such as cold‑start entities and temporal drift
• Formalize sales execution concepts (deal stages, buyer patterns, rep behaviors) into structured representations and manage ontology versioning
• Partner with engineering, product, and data teams to move prototypes into reliable production systems
• Provide technical mentorship and guide junior team members

Good Fit If You Have

• 2+ years of hands‑on experience applying knowledge graphs or graph‑based learning in production
• PhD in Computer Science, NLP, Machine Learning, or a related field
• Experience with large‑scale unstructured text data (e.g., sales calls, emails)
• Published research in top‑tier venues
• Comfort with ambiguity and ability to turn vague product goals into concrete technical problems