Responsibilities Design the analytical architecture of complex solutions in artificial intelligence and data science; Lead thedevelopment, 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 ininnovation, 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 theresults 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 positive 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 isalso 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 inmathematical optimization methods, operations research or decision models at an advanced level an asset; ApplyingAgilemethodologies in a professional context is an asset; Using Dataiku in the context of developing or deploying analytics solutions is an asset. #J-18808-Ljbffr

📌 Data Science Chief Expert, Cx (Montreal)
🏢 COFOMO
📍 Montreal