Modelling Data Scientist, Vice President - AI Labs
$167,500–$167,500 year
On-siteNew York City, New York, United States
Job Summary
Lead major AI/ML initiatives by designing, building, and evaluating agentic workflows and tool-use systems on complex datasets. Translate business needs into well-scoped solutions, manage the full lifecycle from prototyping to productionization, and define evaluation frameworks for agent quality and safety. Collaborate with engineers and stakeholders to ensure production-grade reliability, observability, and compliance with firm-wide processes. Present technical work to senior leaders and drive thought leadership through publications and presentations. This role sits within BlackRock's AI Labs, a hybrid team of scientists and engineers focused on generative AI, optimization, and risk management to revolutionize asset management.
Required Qualifications
- PhD in a quantitative field (machine learning, artificial intelligence, mathematics, statistics, economics, computer science, physics, engineering, or related field) with 4+ years of professional experience
- MS degree in a quantitative field plus 7+ years of professional experience in machine learning, artificial intelligence, or other aspects of the AI / data science and agent development process
- Strong familiarity with Python programming
- Strong theoretical background in and practical experience using AI, machine learning, optimization, or statistical techniques
- Experience assessing performance of machine learning methods and agentic systems: benchmark construction, metric design, and statistically sound measurement of quality, safety, and reliability
- Ability to work within a team environment, and to collaborate and communicate across cross-functional groups
- 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
Desired Qualifications
- Depth in one or more research areas relevant to our work, e.g. LLM reasoning, reinforcement learning, machine learning, statistical modeling, or quantitative optimization
- Academic publications, preprints, or open-source contributions
- Experience building and deploying AI agents and agentic workflows using open-source frameworks (e.g. LangGraph, Pydantic AI, OpenAI Agents SDK), agent platforms (e.g. Amazon Bedrock, Microsoft Agent Framework, Claude Agent SDK), or comparable tools, including standards such as Model Context Protocol (MCP) for connecting agents to tools and data
- Experience with retrieval systems for agents: retrieval-augmented generation, embedding models, vector databases, and long-term memory
- Experience with machine learning libraries (e.g. PyTorch), cloud platforms (AWS, Azure, GCP), and the analysis of financial or economic data
- Experience building production-grade solutions, and leading technical projects or managing
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