Senior Data Scientist

Recruiting a Senior 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 Lead Data Scientist, Chief Data Scientist or Head of AI)

The Senior Data Scientist is a technical expert specialized in the development and optimization of data science and machine learning algorithms to analyze structured and unstructured data (text, images, tabular data, time series, etc.). Their primary role is to design, implement, and validate robust algorithmic solutions to solve specific business problems, while collaborating with product teams that define priorities through backlogs. They work closely with data engineers to industrialize their solutions and with business teams to understand requirements.

Responsibilities and Missions

1. Develop Advanced Data Science Algorithms

• Design and implement machine learning models (regression, classification, clustering, deep learning) tailored to business use cases.
• Develop specific solutions for processing tabular data, time series, and text.
• Optimize model performance (accuracy, computation time, generalization).
• Validate models using rigorous evaluation methods (cross-validation, A/B testing).

2. Collaborate with Product and Technical Teams

• Work with Product Owners to understand business requirements.
• Participate in prioritization meetings to align work with backlogs.
• Collaborate with data engineers to industrialize solutions.
• Document algorithmic solutions and their use cases.

3. Ensure Quality and Robustness of Models

• Implement validation tests to assess model quality.
• Identify and correct potential biases in algorithms.
• Ensure compliance with best practices (reproducibility, documentation).
• Optimize models for production (latency, scalability).

4. Innovate and Monitor Technological Advances

• Follow latest advancements in data science and machine learning.
• Evaluate new algorithmic approaches (new models, techniques).
• Propose improvements to existing solutions.
• Participate in trainings and conferences to stay updated.

5. Contribute to Continuous Improvement

• Share best practices with the team.
• Mentor junior data scientists on technical topics.
• Participate in code reviews and technical discussions.
• Contribute to technical documentation and collective knowledge.

Examples of Concrete Achievements

• Developed a churn prediction model improving customer retention by 15%.
• Optimized a classification algorithm reducing errors by 20%.
• Created a scoring system for risk assessment, improving accuracy by 25%.
• Automated a data processing pipeline, reducing computation time by 30%.
• Implemented an NLP solution for customer feedback analysis, increasing insights by 40%.