Data Scientist II, Branch Analytics (B3617)

TD•Toronto, ON

8d•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, build, and deploy advanced analytics solutions across cloud-based platforms, translating complex business problems into production-ready data assets, insights, and automated analytics. This role combines strong technical expertise, analytical thinking, and business partnership to deliver measurable outcomes across operations, risk, and performance management.

Requirements

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

Nice To Haves

Undergraduate degree or advanced technical degree (e.g., math, physics, engineering, finance, or computer science)
Graduate degree or progressive project work experience

Responsibilities

Develop scalable analytics solutions using Python, SQL, PySpark and PowerBI within cloud environments (Azure Databricks and enterprise data platforms)
Build curated datasets, automated pipelines, and analytics layers that support operational monitoring, performance insights, and regulatory controls
Analyze large, complex datasets to identify trends, risks, gaps, and optimization opportunities
Translate ambiguous business questions into structured analytical frameworks and actionable deliverables
Embed data quality checks, validation, and automation into all analytics products
Partner cross-functionally with business stakeholders, data engineering teams, and enterprise analytics groups
Communicate insights through dashboards, presentations, and executive-ready storytelling
Continuously improve analytics processes, standards, and reusable data assets
Support a strong risk and control culture through robust analytical monitoring and governance