Vice President, Modelling Data Scientist – AI Labs

BlackRock

AI Labs data scientist developing production AI agents and evaluation systems. Applying machine learning, generative AI, and optimization to BlackRock’s asset management challenges.

Posted 8/20/2026full-timeNew York City • California, New York • 🇺🇸 United StatesLead💰 $167,500 - $225,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in developing and implementing AI/ML solutions, with a strong focus on agentic workflows and performance evaluation. Proficient in collaborating with cross-functional teams to deliver scalable, production-grade solutions while adhering to compliance standards.

Highest-signal resume keywords

PhD In Quantitative FieldPython ProgrammingMachine Learning ExperienceAI Agent DevelopmentCloud Platforms Experience

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

AIMachine LearningStatistical TechniquesOptimizationBenchmark ConstructionMetric DesignRetrieval-Augmented GenerationEmbedding ModelsVector DatabasesLong-Term Memory

Soft Skills

CollaborationCommunicationTeamwork

Tools & Technologies

PyTorchAWSAzureGCPOpen-Source FrameworksAgent Platforms

Industry Keywords

Model Context ProtocolFinancial Data AnalysisEconomic Data Analysis

Tech Stack

Tools & technologies

AWSAzureCloudGoogle Cloud PlatformPythonPyTorch

About the role

Key responsibilities & impact

Collaborate with scientists, engineers, and investment professionals to develop methodologies for client, investor, and operational problems
Own the entire solution lifecycle from scoping, research, and prototyping through scaled implementation, productionalization, and internal review
Deliver major projects and key initiatives for AI Labs
Translate business needs into well-scoped AI/ML solutions with stakeholders
Design, build, and evaluate AI agents and agentic workflows involving tool use, memory, and multi-step reasoning on large datasets
Rapidly prototype agentic solutions using AI/ML tools, agent frameworks, orchestration platforms, and foundation model APIs
Build production-grade, reliable, scalable solutions with engineers and stakeholders while meeting firm-wide compliance processes
Implement agent observability, guardrails, and evaluation pipelines
Define and maintain evaluation frameworks measuring agent quality, safety, and reliability in production
Present technical work to senior leaders and general audiences
Promote thought leadership through white papers, publications, and presentations

Requirements

What you’ll need

PhD in a quantitative field with 4+ years of professional experience OR 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, including benchmark construction, metric design, and statistically sound measurement of quality, safety, and reliability
Ability to work within a team environment and collaborate and communicate across cross-functional groups
Depth in one or more relevant research areas, such as LLM reasoning, reinforcement learning, machine learning, statistical modeling, or quantitative optimization
Experience building and deploying AI agents and agentic workflows using open-source frameworks, agent platforms, or comparable tools
Experience with standards such as Model Context Protocol (MCP)
Experience with retrieval-augmented generation, embedding models, vector databases, and long-term memory
Experience with machine learning libraries such as PyTorch
Experience with cloud platforms such as AWS, Azure, or GCP
Experience with analysis of financial or economic data
Experience building production-grade solutions and leading technical projects or managing

Benefits

Comp & perks

Annual discretionary bonus
Healthcare
Leave benefits
Retirement benefits
Strong retirement plan
Tuition reimbursement
Comprehensive healthcare
Support for working parents
Flexible Time Off (FTO)
Hybrid work model with flexibility to work from home 1 day a week