Senior Director, Data Science and AI - Advisory

About the role

The Senior Director Data Science and AI - Advisory 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.

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.

Requirements

• 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.
• Background in AI governance, model risk management, or responsible AI frameworks is highly desirable.

Qualifications

• Bachelor's degree in a quantitative field (Finance, Economics, Mathematics, Engineering, Computer Science, etc.) or a bachelor’s degree with related applied quantitative experience.
• Master's degree in quantitative, arts, or business field preferred.
• 6-8 years of progressive experience in data science, AI/ML engineering & data, 1+ years of experience with generative AI and Agentic systems, with at least 4+ years in a people leadership role.

Skills

• Strong understanding of machine learning, deep learning, and generative AI techniques.
• Experience with large-scale data processing and analytics tools.
• Knowledge of cloud platforms and services (AWS, Azure, Google Cloud).
• Experience with model versioning, experiment tracking, and model registry management.
• Ability to communicate complex technical concepts to non-technical stakeholders.
• Excellent problem-solving and analytical skills.
• Strong leadership and team management skills.

Benefits

• Comprehensive benefits package including health, vision, and dental insurance, flexible spending accounts, health savings accounts, retirement savings plans, life, and disability insurance programs.
• Competitive pay, which may vary depending on eligibility factors such as geographic location, date of hire, total hours worked, job type, business line, and applicability of collective bargaining agreements.

Pay

$204,000.00 - $240,000.00

Schedule

Full-time