Senior Director, Data Science and AI – Enterprise

Cushman & Wakefield

Senior Director responsible for AI strategy execution in a leading real estate services firm. Focusing on cross-disciplinary AI innovation, governance, and team leadership.

Posted 7/29/2026full-timeRemote • New York • 🇺🇸 United StatesSenior💰 $204,000 - $240,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in leading the architecture, development, and deployment of AI systems, with a strong focus on generative AI, MLOps, and AI governance. Proven ability to drive innovation and establish robust standards for model development and monitoring in enterprise environments.

Highest-signal resume keywords

AI Architecture LeadershipGenerative AI ExperienceMLOps/LLMOps PracticesPeople LeadershipModel Risk Management

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningData PreprocessingFeature EngineeringHyperparameter TuningModel EvaluationModel VersioningExperiment TrackingPrompt EngineeringAutonomous Task-ExecutionMulti-Agent Orchestration

Soft Skills

Innovation ChampionCollaborationCulture of Experimentation

Tools & Technologies

CI/CD PipelinesData PipelinesModel Registry ManagementModel Monitoring Frameworks

Industry Keywords

AI GovernanceResponsible AI FrameworksEnterprise AI RoadmapData DriftConcept Drift

About the role

Key responsibilities & impact

Lead the end-to-end architecture, development, and deployment of AI, including machine learning, GenAI, and Agentic models that are tailored to business use cases
Drive the development of agentic AI systems — including multi-agent orchestration, tool-use, and autonomous task-execution pipelines — to automate complex enterprise workflows
Establish model development standards encompassing data preprocessing, feature engineering, model selection, hyperparameter tuning, evaluation, and documentation
Partner with data engineering teams to ensure robust, scalable, and high-quality data pipelines that support model training and inference
Mature the organization's AIOps (MLOps & LLMOps) capabilities, including CI/CD pipelines for model training, evaluation, deployment, and monitoring
Define and enforce standards for model versioning, experiment tracking, reproducibility, and model registry management
Implement robust model monitoring frameworks to detect performance degradation, data drift, concept drift, and bias in production systems
Serve as an internal AI innovation champion
Build and maintain an enterprise AI roadmap aligned with strategic business objectives
Foster a culture of experimentation through structured ideation programs, hackathons, and proof-of-concept sprints
Partner, support, and execute the organization's AI governance framework

Requirements

What you’ll need

Bachelor's degree in a quantitative field (Finance, Economics, Mathematics, Engineering, Computer Science, etc.) or a bachelor’s degree with related applied quantitative experience
6-8 years of progressive experience in data science, AI/ML engineering & data
1+ years of experience with generative AI and Agentic systems
At least 4+ years in a people leadership role
Demonstrated track record of delivering production AI/ML systems at enterprise scale
Hands-on experience with generative AI, large language models, and prompt engineering in an enterprise context
Experience building or scaling agentic AI systems
Proven experience establishing MLOps/LLMOps practices
Background in AI governance, model risk management, or responsible AI frameworks is highly desirable

Benefits

Comp & perks

health, vision, and dental insurance
flexible spending accounts
health savings accounts
retirement savings plans
life and disability insurance programs
paid and unpaid time away from work