Chief Data Scientist

Recruiting a Chief Data Scientist requires a thorough understanding of the role. The following is a very general summary, which should be adapted to your specific context.
(Reporting to the Chief Data Officer)

The Chief Data Scientist is the scientific and technical leader responsible for developing data science, machine learning, and AI algorithms that analyze data and extract actionable insights. Their primary role is to design, optimize, and oversee the implementation of advanced algorithms to solve complex business problems, automate processes, and create value from data. They lead teams of data scientists, ML engineers, and GenAI experts, while operating in close agile (matrix) collaboration with product teams who have authority over the backlogs of their team members.

Responsibilities and Missions

1. Develop and Optimize Data Science and AI Algorithms

Design advanced algorithms (ML, deep learning, NLP, GenAI) to analyze data and solve business problems.
Optimize existing models to improve accuracy, performance, and scalability.
Oversee technical development ensuring robustness and reproducibility.
Evaluate and select the best algorithmic approaches based on use cases.

2. Industrialize Algorithms into Production-Ready Products

Collaborate with Data Engineering to integrate algorithms into production pipelines (MLOps).
Ensure quality and performance of models in production.
Automate deployment and monitoring of algorithms.
Document algorithms and their use cases.

3. Collaborate in Agile/Matrix Mode with Product Teams

Work closely with Product Owners and teams having authority over backlogs.
Provide algorithmic expertise to refine data science product requirements.
Participate in agile ceremonies to align algorithmic developments.
Ensure technical constraints are considered in planning.

4. Lead and Grow Technical Teams

Recruit and train data scientists, ML engineers, and GenAI experts.
Structure teams by domain of expertise (NLP, computer vision, etc.).
Mentor team members on best practices.
Promote a culture of innovation and technical excellence.

5. Ensure Ethics and Compliance of Algorithms

Define ethical standards for algorithm usage.
Ensure compliance with regulations (GDPR, AI laws).
Assess and mitigate bias and security risks.
Document processes to ensure traceability and compliance.

6. Innovate and Anticipate Technological Advances

Monitor advancements in AI and data science (GenAI, LLMs, etc.).
Lead pilot projects to test new approaches.
Collaborate with external partners to accelerate innovation.
Represent the company at technical events.

Examples of Concrete Achievements

Developed a demand forecasting algorithm reducing excess inventory by 30%.
Designed a personalized recommendation model increasing sales by 20%.
Optimized a document processing algorithm reducing errors by 95%.
Created a bias evaluation framework improving fairness by 40%.
Developed a content generation algorithm reducing production time by 40%.

Required Skills and Qualities

Deep technical expertise in ML/AI algorithms and associated tools.
Rigorous scientific approach to algorithm design and validation.
Ability to collaborate in agile/matrix mode with product teams.
Sensitivity to ethical and regulatory issues.
Curiosity about technological innovations.