Data Scientist II, Branch Analytics (B3617)

TD•Toronto, ON

5d•CA$81,600 - CA$115,200•Onsite

About The Position

The Branch Analytics team within Analytics, Insights and Artificial Intelligence (AI2) provides consultative, data-driven solutions to support operational excellence, regulatory adherence, and performance optimization across TD’s branch network. The team partners closely with business stakeholders to transform large-scale enterprise data into scalable analytics products, actionable insights, and automated monitoring solutions that directly drive business value. The Data Scientist II, Branch Analytics will design and deliver end-to-end analytics solutions combining data science, advanced analytics, and business intelligence (BI). This role applies statistical analysis, machine learning, and data-driven experimentation to solve complex business problems, while also translating these insights into scalable dashboards, reporting solutions, and decision-support tools for stakeholders. Success in this role requires strong depth in data science and analytical problem solving, complemented by the ability to deliver intuitive, business-facing BI solutions that drive adoption and impact.

Requirements

Undergraduate degree or advanced technical degree preferred (e.g., math, physics, engineering, finance, or computer science). Graduate degree preferred or progressive project work experience.
3+ years of relevant experience; higher degree education and research tenure can be counted.
Strong organizational skills with the ability to work in a fast-paced environment and manage multiple deadlines and priorities.
Ability to effectively work in teams across the bank with multiple stakeholders and to influence and align others.
Strong risk acumen – challenges the status quo and proactively manages risks.
Ability to work in ambiguity and simplify complex issues.
Strong work ethic and ability to execute with speed.
Proficiency in Python (including Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch, and PySpark) and SQL (writing complex queries, stored procedures, and data extraction).
Hands-on experience with machine learning techniques, including supervised and unsupervised learning, reinforcement learning, and causal inference.
Experience building, training, and deploying machine learning models, including model pipelines, feature engineering, hyperparameter tuning, and model explainability.
Experience with cloud-based data and AI/ML platforms (e.g., Azure, AWS, or GCP), including deploying and managing models in production environments.
Proficiency in tools like Power BI, Tableau, or similar platforms to create impactful visualizations and dashboards.

Responsibilities

Apply statistical analysis, machine learning, and advanced analytics techniques to identify trends, risks, and optimization opportunities
Design and build analytical models, experimentation frameworks, and data-driven solutions to address complex business problems
Develop and maintain curated datasets, feature layers, and analytical data models to support both modeling and reporting use cases
Translate ambiguous business problems into structured analytical approaches, KPIs, and measurable outcomes
Deliver insights through interactive dashboards (Power BI), reporting solutions, and executive-ready storytelling
Partner with stakeholders to define analytical requirements and design data-driven decision frameworks
Ensure data quality, validation, and governance across both analytical models and BI outputs
Operationalize analytics through automated pipelines, monitoring solutions, and repeatable reporting processes
Continuously improve analytical methodologies, data assets, and reporting efficiency
Support risk and control objectives through analytical monitoring and insight generation