Vice President, AI Data Scientist, Private Markets

Overview

As Vice President, AI Data Scientist, you will be a senior player-coach and technical owner for a portfolio of graph-enabled AI capabilities across private-markets data acquisition, research and data-management workflows. You will build and scale enterprise-grade multi-agent LLM systems, knowledge graphs and predictive models that turn fragmented private-markets data into research and decision intelligence.

What you'll do

• Own the technical vision and delivery roadmap for graph-enabled AI and applied data science across private-markets data acquisition, research and data-management workflows.
• Work forward-deployed with researchers, data specialists and product teams to frame ambiguous problems, define measurable outcomes and iterate rapidly from evidence to production.
• Architect and deliver advanced LLM and multi-agent workflows that plan, retrieve, traverse graphs, use tools, reason across evidence, verify conclusions and produce source-grounded outputs.
• Define how agents interact with knowledge graphs, vector and relational stores, document evidence, search services and approved external sources, with clear controls for access, state, memory and tool use.
• Build and evolve enterprise knowledge-graph capabilities, including domain modelling, entity resolution, relationship discovery, graph quality, provenance, temporal context and integration with operational data products.
• Develop graph-based retrieval and context-assembly methods that improve factual grounding, entity disambiguation and multi-step reasoning across complex private-markets relationships.
• Design predictive and probabilistic models over entity networks to identify patterns, prioritise investigation and surface potential relationships, while clearly separating observed facts from modelled signals.
• Lead applied research and experimentation across model adaptation, retrieval, graph machine learning, agent evaluation and reasoning, converting successful approaches into reusable production components.
• Establish evaluation as a core engineering discipline using representative datasets, explicit quality standards, regression testing, human feedback, provenance, observability and monitored production outcomes.
• Own production quality across reliability, latency, cost, security and maintainability, and make pragmatic build, buy, partner and reuse decisions.
• Partner with engineering, product, risk, privacy, information security, legal and compliance colleagues to ensure appropriate governance, human oversight and accountable use of AI.
• Mentor data scientists and AI engineers, provide technical direction and design review, strengthen hiring and capability development, and communicate complex trade-offs clearly to senior stakeholders.

What you'll need

• Substantial experience designing, building and operating production AI and machine-learning systems in an enterprise, SaaS, data-product or similarly complex environment.
• Demonstrated leadership of technically complex AI initiatives from problem discovery and experimentation through deployment, monitoring, adoption and measurable outcomes.
• Deep expertise in LLM applications and agentic systems, including retrieval, tool use, planning, orchestration, memory or state management, structured outputs and evaluation of non-deterministic behavior.
• Strong practical experience with knowledge graphs and graph data science, including graph modelling, entity and relationship resolution, graph retrieval or traversal, reasoning over connected data and graph quality controls.
• Experience developing predictive models on relational or networked data, with disciplined treatment of uncertainty, explainability, bias, leakage and model validation.
• Strong grounding in modern machine learning, experimentation and software engineering, with the ability to write production-quality code and work effectively across data, model, service and application layers.
• Experience with unstructured and multimodal information, semantic retrieval, provenance and data-quality engineering in workflows where domain experts provide ground truth and structured feedback.
• A strong record of working directly with users or customers to translate ambiguous, high-value needs into usable products, and of converting domain-specific solutions into reusable capabilities.
• Sound judgment on architecture and model trade-offs across quality, latency, cost, security, maintainability and operational risk.
• Ability to lead through influence, mentor technical talent and communicate clearly with research, product, engineering and executive audiences.
• Curiosity and the ability to build domain depth quickly are essential.
• Commitment to responsible AI, data provenance, privacy, security and meaningful human control in high-trust enterprise workflows.

Nice to have

• Understanding of private markets, alternative investments, financial data or adjacent institutional-investment workflows is highly desirable.

Details

• Location: Bengaluru, India.
• Hybrid work model: employees are currently required to work at least 4 days in the office per week, with the flexibility to work from home 1 day a week.
• Some business groups may require more time in the office due to their roles and responsibilities.