Director, Agentic AI, Data Science Lead, AI Labs

About This Role

AI Labs Overview

For more than three decades, BlackRock has combined world-class talent and technology to redefine what's possible in asset management. Today, AI is reshaping every aspect of our industry, from investments and client experiences to operations, risk management, and product innovation. BlackRock AI Labs sits at the center of that transformation.

AI Labs is BlackRock's advanced AI science and engineering organization. We partner with teams across the firm to solve some of the most complex and consequential challenges in financial services, applying generative AI, machine learning, optimization, and statistics to deliver measurable business impact. Our mission is simple but ambitious: combine human and machine intelligence to revolutionize asset management.

What sets AI Labs apart is our ability to move from idea to impact. We are not a research lab detached from the business, nor are we a traditional software engineering organization. We are a hybrid team of scientists and engineers who build solutions that run at scale in production environments and create capacity for our business partners. From AI-powered investment insights and risk management solutions to next-generation agentic workflows - our work is helping to shape the future of BlackRock.

Our teams work on some of the firm's highest priority strategic initiatives, including large-scale generative AI systems, intelligent agents, predictive analytics, optimization engines, operational risk solutions, and AI platforms that accelerate innovation across BlackRock. We help define best practices, establish technical standards, and enable teams throughout the organization to adopt AI safely, effectively, and at scale.

AI Labs has offices in New York, Edinburgh, Atlanta, San Francisco and Seattle.

We are looking for candidates with unique backgrounds and diverse skill sets with fresh perspectives to accelerate and amplify our efforts to make an impact at BlackRock.

Job Description

As a Data Science Director, you will take end-to-end ownership of flagship AI Labs initiatives - from initial scoping and stakeholder alignment, through research, prototyping, and technical build-out, to testing, iteration, and production deployment. You will partner directly with business and technology stakeholders to translate ambiguous problems into well-scoped programs of work, own the solutions architecture, and ensure delivery is reliable and at scale. Alongside this technical and delivery accountability, you will manage and mentor more junior AI scientists, helping to grow their skills, guide their career development, and build a strong bench of talent within AI Labs. This role reports to the Head of Science in AI Labs and manages a team of 3-6 AI scientists.

Responsibilities

Own flagship AI Labs initiatives end-to-end: scoping, stakeholder alignment, research, prototyping, build, and production deployment.
Partner with senior business and technology stakeholders to turn ambiguous problems into scoped programs with agreed priorities and success criteria.
Lead the design, build, and evaluation of AI agents and agentic workflows - tool use, memory, multi-step reasoning - on large, complex datasets, iterating as findings emerge.
Partner with engineering to make solutions production-grade and compliant, with observability, guardrails, and evaluation pipelines in place.
Manage and mentor AI scientists, guiding technical approach and career growth to build the bench in AI Labs.
Communicate technical work and outcomes to senior leaders and general audiences, including white papers, publications, and presentations.

Qualifications

Required experience

PhD in a quantitative field (machine learning, AI, statistics, computer science, physics, engineering) with 8+ years of professional experience OR
MS degree in a quantitative field plus 11+ years of professional experience in machine learning, artificial intelligence, or other aspects of the AI / data science and agent development process.
Track record of leading complex, ambiguous technical initiatives end-to-end, from scoping through production, and delivering measurable business impact.
Experience managing or mentoring AI scientists or similar technical talent, including supporting their career growth and development.
Strong familiarity with Python programming, and hands-on experience guiding solutions architecture for production-grade AI/ML systems.
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.

Additional experience we value: you don't need all of these. We're looking for depth in some areas and the range to grow into the rest.

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, Tensorflow), cloud platforms (AWS, Azure, GCP), and the analysis of financial or economic data.
Experience helping define technical standards, best practices, or ways of working adopted across multiple teams, and exposure to financial services or another regulated environment.

Our Benefits

To help you stay en