Vice President, AI Data Scientist, Private Markets

About this role VP – AI/ML - Private Markets Forward-deployed AI leadership for connected private-markets intelligence Build AI systems that connect fragmented evidence, reason across complex entity networks and turn trusted private-markets data into scalable research and decision intelligence.

Your team You will join a forward-deployed, multidisciplinary AI and data science team working at the intersection of advanced AI, knowledge engineering, private-markets research and enterprise product delivery.

We partner directly with researchers, data specialists, product leaders and engineers to solve high-value problems across funds, investors, fund managers, companies, deals, service providers and the relationships that connect them.

Our focus is to transform fragmented public, contributed and derived information into trusted, connected and decision-ready intelligence.

We combine domain expertise, strong data foundations, human judgment and rigorous engineering to build capabilities that can be reused across data acquisition, research, validation, enrichment and data management.

You will work in an environment where applied research is expected to become dependable production capability, and where quality, provenance, security and responsible human oversight are designed in from the start.

Your role & impact As Vice President, AI Data Scientist, you will be a senior player-coach and technical owner for a portfolio of graph-enabled AI capabilities.

You will work directly with users to identify consequential problems, test the right technical approach and lead delivery from discovery through governed production and adoption.

Your core mandate is to research, build and scale enterprise-grade multi-agent LLM systems, with particular depth in knowledge graphs, graph-enabled agent interactions and predictive modelling across connected entity networks.

You will determine when graph reasoning, retrieval, machine learning, agentic orchestration or deterministic services best fit the problem, rather than defaulting to complexity.

Your impact will be visible in stronger data coverage and quality, faster and more effective research, better relationship intelligence, scalable automation and new insights that help BlackRock Preqin remain at the forefront of private-markets data and technology.

You will also raise the technical bar by developing people, codifying reusable patterns and translating research advances into practical enterprise capability.

Your responsibilities 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 can 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 obs erved 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.

Required experience We are looking for a hands-on technical leader who combines research depth, production judgment and customer-facing problem solving.

You should bring: 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 e