Head of Data & AI Product

Recruiting a Head of Data & AI Product requires a thorough understanding of the role. The following is a very general summary, which should be adapted to your specific context.

The Head of Data & AI Product reports directly to the Chief Data & AI Officer (CDAO) and is responsible for managing Data & AI products throughout their lifecycle.
They lead in a transversal and matrix manner the teams of Product Owners, squads (composed of data scientists, data engineers, MLOps engineers) and Product Managers to ensure the effective delivery of Data & AI products, their adoption by business units, and the measurement of their ROI.

Core Responsibilities

a. Transversal Leadership and Product Team Management

Agile and Matrix Leadership:

Lead Product Owners and squads (data scientists, data engineers, MLOps engineers) in an agile manner to ensure the delivery of Data & AI products on time and according to quality standards.
Organize agile rituals (sprints, reviews, retrospectives) and ensure alignment of teams on product objectives.

Product Managers Management:

Supervise Product Managers, who act as interfaces with business units to gather needs, prioritize features, and ensure end-user satisfaction.

b. Definition and Prioritization of Data & AI Products

Product Vision and Roadmap:

Define the product vision and roadmap in collaboration with the Head of Data & AI Strategy and business units.
Prioritize features and improvements based on their business impact, technical feasibility, and user feedback.

Functional and Technical Specifications:

Write user stories and technical specifications in collaboration with technical teams and business units.
Validate prototypes and beta versions to ensure they meet business expectations.

c. Deployment and Adoption of Data & AI Products

Launch and Deployment:

Plan and supervise the launch of products in collaboration with marketing, sales, and technical teams.
Define deployment strategies (phased, pilot, generalized) to maximize adoption and minimize risks.

Adoption and Training:

Implement training and support programs to ensure successful adoption of products by end users.
Collaborate with business units to identify and remove barriers to adoption.

d. Measurement of Impact and ROI

KPI Tracking:

Define and monitor key performance indicators (KPIs) to measure adoption, usage, and business impact of products (e.g., usage rate, user satisfaction, productivity gains).
Analyze usage data to identify opportunities for improvement or new use cases.

ROI Measurement:

Evaluate the return on investment (ROI) of Data & AI products and present results to stakeholders (management, business units).
Propose strategic adjustments to maximize the business value of products.

e. Collaboration with CDAO Teams and Business Units

Alignment with the Head of Data & AI Strategy:

Work closely with the Head of Data & AI Strategy to ensure the product roadmap is aligned with the overall Data & AI strategy.

Coordination with Technical Teams:

Collaborate with the Head of AI Factory to ensure products are developed and industrialized according to best practices (MLOps, DevOps).
Work with the Head of Data Governance to ensure products comply with governance and compliance standards.

Interface with Business Units:

Act as the main point of contact for business units through Product Managers, to gather needs, validate solutions, and ensure user satisfaction.

Concrete Achievements Examples

Successful Launch of a Personalized Recommendation Product:
Led a squad of 5 data scientists and 3 data engineers to develop and deploy an AI-based recommendation system, increasing sales by 15% in 6 months.
Implemented a training program for sales teams, ensuring 90% product adoption.
Deployment of a Predictive Analytics Platform:
Prioritized and delivered a platform enabling business teams to predict sales trends, reducing forecasting errors by 20%.
Measured the product’s ROI, demonstrating a €500K savings on operational costs in one year.
Improvement of a BI Dashboard User Experience:
Completely redesigned an existing dashboard in collaboration with end users, increasing its usage rate by 30%.
Established KPIs to track adoption and business impact, with monthly reviews to adjust features.
Integration of a Predictive Maintenance Model:
Coordinated with technical teams to integrate a predictive maintenance model into an industrial software, reducing downtime by 25%.
Trained maintenance teams and measured the impact, achieving a positive ROI in less than 8 months.