Lead Data Scientist

Recruiting a Lead 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 Head of AI or Chief Data Scientist)

The Lead Data Scientist is a technical expert specialized in the development and optimization of data science, machine learning, and AI algorithms to analyze structured and unstructured data (text, images, tabular data, time series, etc.). Their primary role is to design, implement, and industrialize algorithmic solutions to solve complex business problems, while collaborating in agile mode with product teams who have authority over their team’s backlogs. They lead a team of data scientists and ML engineers, and work closely with data engineering and product teams to deliver robust and scalable solutions.

Responsibilities and Missions

1. Develop and Optimize Data Science Algorithms

Design machine learning and AI algorithms (supervised, unsupervised, deep learning) to analyze various data types.
Develop specific solutions for processing tabular data, time series, text, and images.
Optimize model performance (accuracy, computation time, scalability).
Implement rigorous evaluation methods to validate algorithm quality.

2. Industrialize Algorithmic Solutions

Collaborate with Data Engineering teams to integrate algorithms into production pipelines.
Develop MLOps solutions for model deployment and monitoring.
Automate validation and testing processes for algorithms.
Document algorithmic solutions and their use cases.

3. Collaborate in Agile Mode with Product Teams

Work closely with Product Owners who manage the backlogs of their team.
Translate business needs into algorithmic solutions.
Participate in agile ceremonies (sprint planning, reviews, retrospectives).
Align algorithmic developments with product priorities.

4. Lead and Grow the Technical Team

Mentor data scientists and ML engineers in the team.
Promote best practices in algorithm development.
Organize training sessions on new data science techniques.
Evaluate technical skills and propose development plans.

5. Ensure Quality and Ethics of Solutions

Implement validation methods to assess model quality.
Identify and mitigate potential biases in algorithms.
Ensure compliance with regulations (GDPR, AI ethics).
Document development processes to ensure traceability.

6. Innovate and Monitor Technological Advances

Follow the latest advancements in data science and machine learning.
Evaluate new algorithmic approaches (GenAI, new models, etc.).
Lead PoCs (Proof of Concept) to test innovative solutions.
Participate in conferences and events to stay updated.

Examples of Concrete Achievements

Developed a prediction algorithm using time series, improving forecast accuracy by 25%.
Implemented a classification model for tabular data analysis, reducing errors by 30%.
Created a recommendation system based on collaborative filtering algorithms, increasing user engagement by 20%.
Optimized a data processing pipeline, reducing computation time by 40%.
Developed an anomaly detection model for real-time data analysis, improving detection by 35%.

Required Skills and Qualities

Technical expertise in ML/AI algorithms (supervised, unsupervised, deep learning).
Proficiency in languages and frameworks (Python, R, TensorFlow, PyTorch, scikit-learn).
Experience with MLOps pipelines and data engineering tools.
Ability to collaborate in agile mode with product teams.
Sensitivity to ethical issues and model quality.
Curiosity about technological innovations in data science.