Senior Director Data Science and AI

Cushman & Wakefield

20h•$204,000 - $240,000

About The Position

The Senoir Director, Data Science & AI is a senior technical leader responsible for executing the organization's AI strategy. This role leads a multidisciplinary team of data scientists, ML engineers, and AI practitioners to build, deploy at scale, operate, and govern AI solutions across the enterprise. The Director champions AI innovation across the enterprise — spanning traditional machine learning, generative AI, and agentic systems — while maintaining the operational rigor, governance frameworks, and ethical standards required of a modern AI-driven organization.

Requirements

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, from inception through deployment and ongoing operations.
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.

Nice To Haves

Master's degree in quantitative, arts, or business field preferred.
Background in AI governance, model risk management, or responsible AI frameworks is highly desirable.

Responsibilities

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, with automated alerting and retraining triggers.
Manage cloud AI/ML platform costs and optimize infrastructure utilization across training, fine-tuning, and inference workloads.
Serve as an internal AI innovation champion — identifying high-value use cases across business functions and translating them into AI-powered solutions.
Build and maintain an enterprise AI roadmap aligned with strategic business objectives, balancing quick wins with long-term capability building.
Foster a culture of experimentation through structured ideation programs, hackathons, and proof-of-concept sprints, ensuring rapid validation and responsible scaling of AI initiatives.
Collaborate with product and technology leadership to embed AI capabilities into core enterprise capabilities and customer-facing products.
Partner, support, and execute the organization's AI governance framework, including policies for model risk management, fairness, explainability, privacy, and security.
Lead AI risk assessments and ensure all models in production meet internal standards and applicable regulatory requirements.
Partner with Legal, Compliance, and Risk teams to manage data privacy obligations (GDPR, CCPA), intellectual property considerations for generative AI outputs, and third-party AI vendor due diligence.
Champion sound AI principles organization-wide, ensuring that human oversight and accountability are embedded in every stage of the AI development lifecycle.
Recruit, develop, and retain a high-performing team of AI practitioners.
Establish clear team structure, career paths, and performance frameworks that reward both technical excellence and collaborative impact.
Foster a team culture that values intellectual curiosity, rigorous experimentation, continuous learning, and collaboration.
Serve as a technical mentor and thought leader for technical and business teams in Technology and across the business.
Build strong cross-functional partnerships with technology and business unit leaders to ensure AI initiatives are well-defined and aligned with business priorities.
Define and track KPIs and OKRs for the Data Science & AI function, providing regular reporting on model performance, operational health, and business impact to teams and leaders across the organization.