Director of Applied Science and Engineering - Knowledge Graphs & AI
O
OutreachB2B SaaS / Revenue Intelligence
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
- Required Skills
- PythonMachine LearningGoNLP
Requirements
- PhD in Computer Science, Machine Learning, NLP, or related field with focus on knowledge representation, reasoning, or graph learning.
- 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.
- Demonstrated track record of building and shipping knowledge graph, NLP, or graph ML systems at production scale.
- Deep expertise in at least three of: knowledge graph construction, entity resolution, information extraction, graph neural networks, temporal reasoning, representation learning, or recommender systems.
- Proficiency in Python and Golang.
- Experience with graph databases or query languages such as Neo4j, SPARQL, or Cypher.
- Strong engineering fundamentals with ability to write production-quality code.
- Proven experience recruiting, developing, and retaining top applied science talent.
- Executive communication skills with ability to present to C-suite and board audiences.
- Strong ownership and ability to operate in highly ambiguous environments.
Responsibilities
- Define and own the multi-year technical roadmap for the Knowledge Graph platform.
- Build, hire, and lead a high-performing team of applied scientists and research engineers.
- Drive the design of per-tenant knowledge graph schemas, ontologies, and data models.
- Oversee pipelines for information extraction from unstructured conversational and document data.
- Lead the development of reasoning and inference layers for AI-driven revenue intelligence.
- Direct research into graph-based models and representation learning to support recommendation tasks.
- Establish processes for moving research from exploration to production deployment.
- Partner with cross-functional leadership in Engineering, Product, and Data to align science investments with business goals.
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