Director of Applied Science and Engineering - Knowledge Graphs & AI
O
OutreachAI software
Listing location: Hyderabad; Workplace type: Remote; Structured job location: HyderabadFull-TimeDirector
Salary not disclosed
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Job Details
- Experience
- 10+ years of experience in applied science or machine learning, with at least 3 years in a people leadership role managing teams of 5+ applied scientists or research engineers.
- Required Skills
- PythonGo
Requirements
- PhD in Computer Science, Machine Learning, NLP, or a related field, focused on an area such as knowledge representation and reasoning, graph neural networks, information extraction, recommender systems, or conversational AI and dialogue systems.
- 10+ years of experience in applied science or machine learning.
- At least 3 years in a people leadership role managing teams of 5+ applied scientists or research engineers.
- Track record of building and shipping knowledge graph, NLP, or graph ML systems at production scale with measurable business outcomes.
- Deep expertise in at least three of: knowledge graph construction, entity resolution, information extraction, graph neural networks, temporal reasoning, representation learning, or recommender systems.
- Strong engineering fundamentals and ability to write production-quality code.
- Proficiency in Python or Golang.
- Experience with graph databases or query languages, such as Neo4j, SPARQL, or Cypher.
- Experience recruiting, developing, and retaining applied science talent and growing individual contributors into senior technical leaders.
- Ability to communicate complex research concepts as business impact narratives for C-suite and board audiences.
- Take end-to-end responsibility for research and model development, from problem formulation and data analysis through experimentation, production deployment, and performance monitoring.
Responsibilities
- Define and own the multi-year technical roadmap for the Knowledge Graph platform, including entity resolution, temporal reasoning, graph-based learning, and contextual inference.
- Build, hire, and lead a team of applied scientists and research engineers; establish research rigor, career development, and production-impact standards.
- Design per-tenant knowledge graph schemas, ontologies, and data models for the sales execution domain.
- Own decisions on graph databases, query languages, storage engines, and tenant isolation strategies at scale.
- Oversee pipelines that extract structured knowledge from sales calls, emails, and CRM notes, including coreference resolution, relation extraction, event detection, and entity linking.
- Lead reasoning and inference systems for next-best-action suggestions, deal risk scoring, coaching recommendations, competitive intelligence, and agentic AI decision-making.
- Direct research into graph-based models, including GNNs, relational embeddings, link prediction, and temporal graph networks.
- Partner with Engineering, Product, Design, and Data leaders to align science investments with product priorities.
- Establish processes and infrastructure for moving research into production, including experiment tracking, model evaluation, A/B testing, and continuous model improvement.
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