Head of Data & AI

Managing the recruitment of a Head of Data & AI (who may be a Chief Data Officer for a small entity) requires, first and foremost, a thorough understanding of their role. It is summarised here in very general terms and must be adapted to your specific context.

The Head of Data & AI is responsible for the strategy and implementation of Data & AI initiatives within a small or medium-sized company. This role combines strategic and operational responsibilities, aiming to turn data into business value while ensuring governance, compliance, and innovation. Unlike a CDAO, this position is more hands-on, with direct involvement in designing and deploying solutions.

Core Responsibilities

a. Define and Implement Data & AI Strategy

Vision and Roadmap:

Develop a Data & AI strategy aligned with the company’s business objectives, in collaboration with management and business teams.
Prioritize initiatives based on their business impact (e.g., new products, process optimization, customer experience).

Data Governance:

Establish data governance policies (quality, security, accessibility) tailored to the company’s size.
Implement processes to ensure compliance (GDPR, industry standards).

b. Leadership and Management of Data & AI Teams

Team Structuring:

Build and lead a multidisciplinary team (data scientists, data engineers, analysts) or work with external resources (freelancers, consulting firms).
Define roles and responsibilities for effective project execution.

Skill Development:

Train and support teams on best practices in Data & AI.
Promote a data-driven culture within the company.

c. Development and Deployment of Data & AI Solutions

Design and Industrialization:

Oversee the development of Data & AI solutions (dashboards, predictive models, automations).
Industrialize solutions for scalable and reliable deployment (using tools like Dataiku, Databricks, or cloud solutions).

Collaboration with Business Teams:

Work closely with business teams to understand their needs and translate them into technical solutions.
Provide ongoing support to users to maximize tool adoption.

d. Management of Infrastructure and Tools

Data & AI Architecture:

Select and deploy tools and platforms suited to the company’s needs (e.g., Snowflake, Power BI, Python, SQL).
Oversee the maintenance and evolution of data infrastructure (data lakes, pipelines, databases).

Security and Compliance:

Ensure data security and compliance with regulations.
Implement regular audits to identify and correct risks.

e. Value Creation and Impact Measurement

Priority Use Cases:

Lead high-impact Data & AI projects (e.g., customer personalization, predictive maintenance, cost optimization).
Measure and communicate the ROI of initiatives to justify investments.

Innovation and Technological Watch:

Monitor Data & AI trends (e.g., generative AI, automation) and assess their relevance to the company.
Experiment with new solutions to stay competitive.

f. Stakeholder Relations

Internal and External Communication:

Present the Data & AI strategy and achievements to management and business teams.
Collaborate with external partners (technology providers, startups) to accelerate innovation.

Expectation Management:

Align stakeholder expectations with the actual capabilities of the team and tools.
Prioritize projects based on available resources.

Concrete Achievements Examples

Implementation of a Data Platform: Deployment of a centralized solution (e.g., Snowflake + Power BI) to improve data access and reduce operational costs by 20%.
Deployment of a Predictive Model: Development and industrialization of a predictive maintenance model, reducing downtime by 15%.
Automation of Reports: Creation of automated reports for sales teams, increasing productivity by 25%.