Applied AI ML Director - Head of AI & Data Science

Shape the future of workforce decision-making by leading a global team that turns data into trusted insights and measurable business outcomes. Join a team investing in modern artificial intelligence tooling and evaluation practices to deliver faster, safer, and more scalable solutions. Build your leadership brand through high-visibility partnerships, career mobility, and opportunities to grow enterprise impact.
As an Applied AI ML Director - Head of AI & Data Science in the HR Data & Analytics team, you lead a group of data scientists who turn workforce data into insights and recommendations that business leaders use to make evidence-based people decisions. You set the technical agenda, direct the full research lifecycle across a portfolio of projects, and decide where to build, reuse, or partner — balancing fast, commercial delivery with stakeholder partnership and disciplined controls, across a global team.
Our team is building an artificial-intelligence-native operating environment — a template for how a global firm delivers artificial intelligence — which we intend to prove within our own organization before extending it more broadly. That work includes model factories that build and improve models against product benchmarks, a high-bandwidth evaluation factory that investigates edge cases and calibrates results, and agentic engineering tools deployed responsibly within a regulated environment. The guiding principle is to use AI to accelerate AI: invest in the tooling and infrastructure that let the team build and deliver faster. This role sets the vision and technical direction for that agenda and leads the people who carry it out.
Job Responsibilities
Act as the hands-on leader of a team that delivers commercial outcomes using AI and machine learning solutions across our people-focused product areas
Direct the full research lifecycle, from framing problems and generating hypotheses through experiments, prototypes, and results that reach production
Develop model factories and a high-bandwidth evaluation factory that create, calibrate, and improve models and agents against product benchmarks
Advance agentic, specification-driven, and evaluation-driven development, including multi-agent orchestration deployed responsibly in a regulated environment
Translate models into insights that help reduce costs, expand capabilities, strengthen controls, and improve employee experiences
Promote reuse and partnership of techniques across the firm, and adopt vendor solutions where they add value
Partner with technology teams to architect scalable, cost-effective, enterprise-quality data and analytics systems, including self-service platforms
Align and prioritize research and development resources against the products that matter most and their measurable business impact
Communicate the significance of the team's work to senior leaders, and pursue patents or publications where warranted
Ensure conformance with all applicable controls, policies, and procedures
Required qualifications, capabilities, and skills
7 years of relevant experience in AI and data science
Demonstrated success managing and leading teams
Master's degree or PhD in a quantitative discipline
Expertise with modern artificial intelligence and machine learning algorithms, techniques, and software, applied to enterprise-scale solutions
Hands-on experience with agentic engineering, including agentic coding tools, specification-driven or evaluation-driven development, or multi-agent orchestration
Practical experience with statistical data analysis and experimental design
Practical software development experience in collaborative settings
Excellent communication skills and strong executive presence, with credibility as a thought leader and influencer
Strong project management skills that drive execution and delivery
Preferred qualifications, capabilities, and skills
Experience building automated model-development or model-evaluation pipelines
Experience delivering artificial intelligence and machine learning solutions within a regulated environment
Track record of building partnerships across organizational, functional, and hierarchical boundaries
Experience delivering self-service analytics platforms
Track record of patents or peer-reviewed publications