Data Scientist

The Data Scientist is a technical expert responsible for developing and enhancing iA Clarington’s data architecture and analytics capabilities. This role leads advanced data science initiatives, designs predictive models, and integrates next-generation analytics across business units. The Data Scientist empowers decision-makers with actionable insights, robust forecasting, experimentation-driven insights, and scalable data solutions to support enterprise transformation.

Key Responsibilities (with approximate workload percentages)

1. Advanced Analytics, Modeling & Applied Research — 30%

• Lead hypothesis-driven analyses using statistical inference, hypothesis testing, and exploratory data analysis to uncover patterns and validate business assumptions.

• Design, evaluate, and iterate on predictive and prescriptive models using appropriate statistical, machine learning, and optimization techniques based on problem complexity.

• Conduct applied research to assess, test, and operationalize emerging analytical and AI/ML techniques.

• Select appropriate modeling methodologies based on data structures, business context, and analytical objectives.

• Document model assumptions, methodology, limitations, and interpretability considerations.

2. Experimental Design & Model Validation — 20%

• Design, implement, and analyze experiments (e.g., A/B tests, multivariate tests) to measure causal impact and optimize business strategies.

• Oversee model validation, including performance evaluation, out-of-sample testing, drift detection, and bias and fairness monitoring.

• Establish monitoring frameworks and KPIs to assess ongoing model health and ensure responsible AI use.

3. Data Architecture, Engineering & Integration — 20%

• Design, implement, and enhance scalable, secure data architecture aligned with business goals and regulatory standards.

• Integrate data from Salesforce, Power BI, CRM Analytics, and internal/external systems to produce unified, high-quality datasets.

• Develop and maintain robust data pipelines that support analytics, experimentation, and model deployment.

4. Decision Support & Next Best Action — 10%

• Build and operationalize algorithms recommending optimal business actions for enhanced decision-making.

• Partner with business teams to translate analytical findings into actionable strategies and measurable business outcomes.

5. Visualization, Communication & Stakeholder Engagement — 15%

• Develop dashboards and visualizations in Power BI and CRM Analytics/Salesforce to communicate analytical outcomes clearly.

• Present insights to technical and non-technical stakeholders, ensuring complex concepts are translated into business-friendly recommendations.

• Mentor colleagues on analytical best practices, experimental methods, and model interpretation.

6. Continuous Innovation & Analytics Maturity — 5%

• Evaluate and adopt emerging analytical tools, AI/ML libraries, and research methods.

• Promote a culture of data-driven decision-making and continuous improvement across the organization.