Data Science Expert

Responsibilities

• Design the analytical architecture of complex solutions in artificial intelligence and data science;
• Lead the development, validation, industrialization and continuous improvement of high-value machine learning models;
• Design and develop advanced time-series and forecasting models in large-scale, complex contexts;
• Develop and implement Reinforcement Learning solutions adapted to optimization, control or decision-making issues;
• Act as an expert advisor to clients in order to identify the best analytical approaches according to their business objectives;
• Define technical orientations, quality standards and best practices in data science, quantitative modeling, MLOps and artificial intelligence;
• Oversee the full model lifecycle: data mining, experimentation, training, validation, deployment, monitoring, and continuous improvement;
• Collaborate with architects, data engineers, and subject matter experts to ensure robust integration of developed solutions;
• Perform technical reviews, ensure the scientific quality of deliverables and support teams during mandates with the highest levels of complexity;
• Mentor and mentor data scientists to foster their professional development and knowledge sharing;
• Participate in innovation, applied research and technology watch activities in order to integrate relevant advances in artificial intelligence;
• Contribute to business development activities, technical proposals and strategic recommendations for clients;
• Popularize the results to technical and executive audiences.

Profile we are looking for

• Hold an undergraduate degree in mathematics, statistics, actuarial science, operations research, computer science, software engineering, physics or a related quantitative field (mandatory);
• Possess more than ten (10) years of experience in data science, machine learning, or artificial intelligence;
• Demonstrate advanced expertise in time series modeling, forecasting, and temporal data analysis (required);
• Have advanced expertise in statistical modeling, probability, quantitative methods, and mathematical optimization;
• Have a strong background in developing machine learning models in a production environment;
• Have experience with MLOps environments (CI/CD, MLflow, DVC, orchestration, model monitoring, and reproducibility);
• Experience with cloud platforms (Azure, AWS or GCP) applied to artificial intelligence solutions;
• Have experience with relational and non-relational databases;
• Master the main algorithms of supervised, unsupervised and deep learning;
• Demonstrate a good understanding of modern data architectures (Data Lake, Lakehouse, and data pipelines);
• Be proficient in Python as well as the main machine learning frameworks;
• Be able to design and develop robust, high-performance and scalable solutions.
• Have a background in actuarial science (strong asset);
• Have a master's degree in statistics, mathematics, actuarial science, artificial intelligence, machine learning, or another quantitative field (strong asset);
• a PhD is also an asset;
• Demonstrate advanced expertise in Reinforcement Learning applied to real-world problems an asset;
• Have experience in the energy, trading, or quantitative finance sector an asset;
• Knowledge of generative models (LLM, generative AI, and agentic systems) an asset;
• Proficiency in mathematical optimization methods, operations research or decision models at an advanced level an asset;
• Applying Agile methodologies in a professional context is an asset;
• Using Dataiku in the context of developing or deploying analytics solutions is an asset.