Data Scientist II
About The Position
The Analytics, Insights & Artificial Intelligence (AI2) team delivers data- and analytics-driven insights that support informed decision-making and strategic planning across the organization. Within AI2, the Canadian Personal Banking (CPB) team provides end-to-end analytics support—including reporting, advanced analytics, and AI solutions—to drive measurable business value. The Data Scientist II, reporting to the Senior Manager, Specialized Sales Force (SSF), designs, builds, and deploys advanced analytics solutions on cloud-based platforms. The role translates complex business challenges into production‑ready data assets, actionable insights, and automated analytics. By combining strong technical expertise, analytical rigor, and close business partnership, this role drives measurable improvements in goaling, pipeline optimization, and performance management in support of mobile mortgage specialists, brokers, and builders.
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
- Develop scalable analytics solutions using Python, SQL, and PySpark 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
Benefits
- health and well-being benefits
- savings and retirement programs
- paid time off
- banking benefits and discounts
- career development
- reward and recognition programs
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
