Senior Data Science Advisor

At least six years of relevant experience in actuarial science or data science, including predictive modelling and spatial analytics

Expertise in at least one data analytics programming language (Python or R) and one data manipulation language (SAS or SQL)

Expertise in statistical and machine learning methods, including regression, decision trees, ensemble learning techniques (e.g., random forests, gradient boosting), clustering, and neural networks.

Practical knowledge of leading data science libraries (machine learning, deep learning, natural language processing, spatial analysis, visualization)

Action oriented, Business insight, Complexity, Customer Focus, Differences, Nimble learning

At Desjardins, we believe in equity, diversity and inclusion.

4 weeks of flexible vacation starting in the first year

Defined benefit pension plan that provides predictable, stable income throughout retirement

Group insurance including telemedicine

Reimbursement of health and wellness expenses and telework equipment

Leveraging some of the industry's richest telematics, geospatial, climatological, and insurance datasets, the team tackles complex challenges in individual and portfolio risk modelling while collaborating with universities and technology partners to advance the quantitative approaches of tomorrow.

Explore and evaluate diverse structured and unstructured data sources, assess their relevance and quality, and prepare enriched modelling datasets by leveraging statistical and actuarial expertise alongside feature engineering techniques in support of diverse and complex predictive modelling initiatives.

Analyze client and partner business needs, generate hypotheses and lead initiatives, taking into account the specifics of their operationalization and ensuring coordination during the rollout.

Apply advanced statistical, machine learning, and artificial intelligence techniques to extract insights from data and develop optimized models and segmentation strategies that address business needs.

Design, develop, and implement analytical workflows, scripts, and reusable tools supporting data preparation and analytics, as well as the development, validation, deployment, monitoring, and maintenance of advanced analytical models.

Translate analytical findings into clear recommendations that support strategic decision-making and drive innovation across business sectors.