The Work
The Data Scientist, Growth leads the design, development, deployment, and operationalization of advanced analytics and machine learning solutions that support growth, customer experience, and commercial decision-making. The role translates ambiguous business problems into measurable data science solutions, with a focus on pricing, customer engagement, recommendation systems, experimentation, and model-driven decision support.
The role works across the full analytical lifecycle, from problem framing and exploratory analysis through model development, validation, deployment, monitoring, and production implementation. It also contributes to the modernization, migration, and scaling of machine learning assets and data workflows across cloud platforms and enterprise analytical environments.
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

📌 Interim Data Scientist, Growth (Toronto)
🏢 Go Fractional
📍 Toronto