The Core Responsibilities Lead machine learning model development for growth initiatives, including pricing, customer engagement, recommendation systems, propensity modelling, net promoter score, and related decisioning use cases. • Design and maintain scalable data pipelines, feature engineering frameworks, and workflows for model training, scoring, monitoring, backtesting, and reporting. • Develop and evaluate experimentation frameworks, including A/B testing, holdout analysis, uplift measurement, causal inference, and incrementality measurement. • Perform feature engineering, model selection, validation, calibration, and performance monitoring. • Partner with product, engineering, analytics, marketing, and business teams to define success measures, prioritize opportunities, and operationalize model outputs. • Lead or contribute to migration and modernization of models, analytical assets, pipelines, and workflows, including the transition from Microsoft Fabric and Azure to Google Cloud Platform. • Assess model dependencies and recommend whether assets should be migrated, refactored, rebuilt, consolidated, or retired. • Design production-ready machine learning solutions, including deployment pipelines, automated retraining, monitoring, and performance tracking. • Apply software engineering, data engineering, and machine learning operations practices, including modular design, version control, testing, automation, reproducibility, documentation, and code quality. • Validate migrated or rebuilt solutions by reconciling data, features, model results, and business outputs. • Communicate model and experiment findings to technical and non-technical stakeholders. • Create reusable analytical frameworks, shared code libraries, notebooks, and production-ready assets. • Mentor team members and support knowledge transfer and analytical excellence.





Let's Talk About You Bachelor's or Master's degree in Data Science, Statistics, Computer Science, Mathematics, Economics, Engineering, or a related quantitative field. • Six or more years of total experience in data science, machine learning, advanced analytics, or a closely related discipline. • Strong proficiency in Python, Structured Query Language, statistical analysis, Spark or similar distributed technologies, and scalable data processing. • Experience building, validating, deploying, monitoring, and governing machine learning models in production or production-adjacent environments. • Strong understanding of experimental design, hypothesis testing, causal inference, and measurement frameworks. • Experience with cloud analytical environments such as Google Cloud Platform, Microsoft Azure, Microsoft Fabric, Amazon Web Services, or similar platforms. • Working knowledge of Git-based development, code review, automated testing, dependency management, and continuous integration and deployment. • Experience with customer growth, personalization, pricing, recommendation systems, retention, or similar business applications. • Advanced analytical problem-solving and sound judgment in ambiguous situations. • Ability to explain technical information, trade-offs, migration risks, and insights to technical and non-technical audiences. • Ability to work independently, manage multiple priorities, and deliver high-quality work in a fast-paced environment. • Preferred: experience with cloud migrations, Vertex AI or equivalent capabilities, contemporary machine learning platforms, Docker, infrastructure as code, observability, orchestration, and automated retraining.

What we offer [For full-time permanent roles] Competitive discretionary bonus Market leading RRSP match program Medical, dental, vision, life, and disability benefits Employee Share Purchase Plan

📌 Data Scientist, Growth (12 month contract) (Toronto)
🏢 Equitable Bank
📍 Toronto