Responsibilities
- Technical Vision & Strategy: Define and own the multi-year technical roadmap for Outreach's Knowledge Graph platform, including entity resolution, temporal reasoning, graph-based learning, and contextual inference. Translate business objectives into a coherent applied science strategy that balances research ambition with production delivery.
- Team Leadership: Build, hire, and lead a team of applied scientists and research engineers. Establish team culture, research rigor, career development frameworks, and a high bar for both scientific quality and production impact. Mentor senior ICs into technical leaders.
- Knowledge Graph Architecture: Drive the design of per-tenant knowledge graph schemas, ontologies, and data models tailored to the sales execution domain. Own decisions on graph databases, query languages, storage engines, and tenant isolation strategies at scale.
- Information Extraction at Scale: Oversee pipelines that extract structured knowledge from unstructured conversational and document data (sales calls, emails, CRM notes), including coreference resolution, relation extraction, event detection, and entity linking.
- Reasoning & Inference Systems: Lead the development of reasoning and inference layers over the knowledge graph to power next-best-action suggestions, deal risk scoring, coaching recommendations, competitive intelligence, and agentic AI decision-making.
- Representation Learning & Graph ML: Direct research into graph-based models (GNNs, relational embeddings, link prediction, temporal graph networks) over heterogeneous, multi-relational graph structures to support downstream reasoning, retrieval, and recommendation tasks.
- Cross-functional Leadership: Partner with leaders in Engineering, Product, Design, and Data to align science investments with product priorities. Represent the applied science function in executive reviews, roadmap planning, and technical design reviews.
- Research-to-Production Pipeline: Establish processes and infrastructure for moving from research exploration to production deployment: experiment tracking, model evaluation frameworks, A/B testing, and continuous model improvement loops.
- Industry & Academic Engagement: Keep the team at the frontier of knowledge graph research. Foster connections with the academic community through conference participation, publications, and strategic academic partnerships.
Requirements
- PhD in Computer Science, Machine Learning, NLP, or a related field with a focus on 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, with 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: not just publishing papers, but delivering 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. You can write production-quality code, not just prototype notebooks. Proficiency in Python / Golang; and graph databases or query languages (e.g., Neo4j, SPARQL, Cypher) is required.
- Experience recruiting, developing, and retaining top applied science talent. You have grown ICs into senior technical leaders and built teams with a strong shipping culture.
- Executive communication skills. You can translate complex research concepts into business impact narratives for C-suite and board audiences.
- Comfort with deep ambiguity. You will define the problem space, not just solve well-scoped problems. You thrive when chartering new technical directions from scratch.
- 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.
Nice to Have
- Experience building multi-tenant knowledge graph systems with per-customer isolation and scale requirements.
- Background in sales, revenue, or B2B SaaS domains: understanding of deal cycles, pipeline management, and CRM data models.
- Experience integrating knowledge graphs with LLM-based systems (RAG architectures, tool-augmented generation, agentic frameworks).
- Strong communication skills with the ability to translate research concepts into product impact for cross-functional audiences.
- Publications in top-tier venues (KDD, NeurIPS, ACL, EMNLP, ICLR, WWW, SIGIR, etc.) in knowledge graphs, NLP, or graph learning.
- Experience with graph databases at scale (Neo4j, Amazon Neptune, or similar) including performance tuning, query optimization, and multi-region deployment.
- Familiarity with the Model Context Protocol (MCP) or similar agent-tool integration patterns.
- Track record of building applied science teams from scratch (0→1 team formation).
Additional Information
- Highly competitive salary
- 25 days annual vacation time + sick time and casual leave
- Group medical policy coverage available to employees and up to 5 eligible family members
- OPD benefit covered up to INR 10,000
- Life insurance and personal accident insurance at 3x annual CTC
- 26 weeks of maternity leave pay, and 15 days of paternity leave pay
- Opportunity to be part of company success via the RSU program
- Diversity and inclusion programs that promote employee resource groups like OWN+ (Outreach Women's Network), Adelante (Latinx community), OBX (Outreach Black Connection), Mosaic (AAPI community), Pride (LGBTQIA+), Gender+, Disability Community, and Veterans/Military
- Employee referral bonuses to encourage the addition of great new people to the team
- Fun company and team outings because we play just as hard as we work
