About the role
from listingProvide leadership and direction to our Data Scientists within our HR & Data Analytics team
HR Data & Analytics is a centralized global team responsible for all aspects of workforce data strategy, analytics and reporting, and governance, and includes the development of AI and machine learning based solutions. We have a vision to help make individuals, teams, and businesses at JPMC among the most engaged and productive in the world. Our mission is to deliver data, analytics, and AI that are secure and commercial to empower businesses to make better workforce decisions and enable enhanced employee experiences. As a 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 Significant 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 of building automated model-development or model-evaluation pipelines Experience of 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