Data & AI Product Manager

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

The Data & AI Product Manager reports to the Head of Data & AI Product and is responsible for the operational management of one or more Data & AI products throughout their lifecycle. They work in agile mode and supervise Product Owners, who act as the interface between business teams and technical teams (data scientists, data engineers, MLOps engineers). Their role is to define the product vision, prioritize features based on business needs and value, and ensure the adoption and success of the products.

Core Responsibilities

a. Definition of Vision and Product Roadmap

Understanding Business Needs:

Identify and analyze the needs of end users and business stakeholders to define functional requirements.
Collaborate with business teams to prioritize features according to their business impact.

Product Roadmap Definition:

Develop and maintain a detailed product roadmap aligned with the overall Data & AI strategy.
Define key objectives (OKRs) and success indicators for each product, in collaboration with Product Owners.

b. Management of the Data & AI Product Lifecycle

Supervision of Product Owners:

Supervise Product Owners as the interface between business needs and technical teams.
Ensure that user stories and functional specifications are clearly defined and prioritized.

Agile Development Monitoring:

Participate in agile rituals (sprints, reviews, retrospectives) to ensure development follows the roadmap and meets deadlines.
Validate prototypes and beta versions with end users, in collaboration with Product Owners.

c. Launch and Adoption of Products

Launch Planning:

Coordinate with marketing, sales, and technical teams to plan product launches.
Define deployment strategies (pilot, phased, general rollout) and communication plans to maximize adoption.

Training and Support:

Organize training sessions and provide support materials (documentation, tutorials) for end users.
Gather user feedback to identify improvement opportunities and adjust products accordingly.

d. Measurement of Impact and ROI

KPI Definition:

Establish key performance indicators (KPIs) to measure adoption, usage, and business impact (e.g., usage rate, user satisfaction, productivity gains).
Analyze usage data to assess product effectiveness and identify new opportunities.

ROI Evaluation:

Calculate and present ROI to stakeholders (management, business teams).
Recommend strategic adjustments to maximize business value.

e. Collaboration with Business Teams and Product Owners

Interface with Business Teams:

Act as the main point of contact for business teams to gather needs and ensure products meet them.
Organize workshops and user reviews to validate features and collect feedback.

Coordination with Product Owners:

Work closely with Product Owners to translate business needs into technical requirements.
Ensure priorities and objectives are clearly communicated to technical teams.

Examples of Concrete Achievements

Launch of a Personalized Recommendation Product:
Supervised 2 Product Owners and collaborated with a team of 3 data scientists and 2 data engineers to deploy an AI-based recommendation system, increasing sales by 10% in 3 months.
Organized training sessions for sales teams, ensuring 85% adoption.
Improvement of a BI Dashboard:
Redefined functional requirements with end users and Product Owners, increasing dashboard usage by 25%.
Established KPIs to monitor adoption and business impact, with monthly reviews to refine features.
Deployment of a Predictive Maintenance Model:
Coordinated with Product Owners and technical teams to integrate a predictive maintenance model into industrial software, reducing downtime by 20%.
Measured ROI, demonstrating €300K savings on operational costs in one year.