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Data Scientist - Job Opportunity at Royal Bank of Canada

Toronto, Canada

Full-time

Senior

Posted: June 7, 2025

Hybrid

CAD 110,000 - 140,000 annually, considering this is a senior-level data scientist position at Canada's largest bank, located in Toronto's premium financial district. The role's specialized focus on audit analytics and compliance, combined with requirements for advanced AI/ML expertise and the prestigious nature of working within RBC's Chief Audit Executive group, positions this role at the upper tier of data science compensation in the Canadian market.

Benefits

Comprehensive Total Rewards Program providing competitive financial incentives including performance-based bonuses that directly correlate to individual and team achievements

Flexible benefits package allowing customization based on personal needs and life circumstances, providing superior value compared to rigid benefit structures

Competitive compensation structure positioning employees favorably within the financial services market

Commission-based rewards where applicable, creating direct alignment between performance and financial outcomes

Stock participation opportunities providing long-term wealth building potential and ownership alignment with company success

Leadership development through dedicated coaching programs and structured management opportunities for accelerated career progression

Dynamic collaborative work environment fostering innovation and high-performance team culture that attracts top talent

Progressive learning opportunities with challenging work assignments that build marketable skills and expertise

Key Responsibilities

Drive strategic data science initiatives within RBC's Chief Audit Executive Group, directly impacting risk management and compliance frameworks that protect billions in assets and ensure regulatory adherence

Architect and deploy end-to-end machine learning solutions using advanced supervised and unsupervised methods, transforming raw data into actionable business intelligence that influences executive decision-making

Pioneer the implementation of Generative AI technologies to revolutionize internal audit processes, creating competitive advantages through automation and enhanced analytical capabilities

Lead cross-functional collaboration initiatives with business and operational units, designing innovative solutions that optimize critical processes and drive measurable efficiency improvements across the organization

Spearhead research-based projects utilizing cutting-edge machine learning methodologies, delivering tangible outcomes that advance the bank's technological capabilities and market position

Develop and maintain production-scale data pipelines and statistical models that process large-scale datasets, ensuring robust and reliable analytical infrastructure supporting mission-critical audit functions

Execute comprehensive data mining, statistical analysis, and pattern recognition projects that identify emerging risks and compliance violations before they impact business operations

Requirements

Education

At least an undergraduate degree (PhD. or Masters preferred) in a quantitative field (Engineering, Statistics, Mathematics, Computer Science, Economics, Sociology, Psychology etc.)

Experience

Advanced level experience with machine learning model development and production-level code implementation

Required Skills

Advanced programming skills (python, pyspark) Experience in developing machine learning models using supervised and unsupervised approaches Experience with data preprocessing, feature and representation learning, anomaly/outlier detection Curiosity, strong data sense, critical thinking, and technical documentation skills A passion for simplifying and automating work, making things better, continuous learning, solving open-ended problems, improving efficiency, and helping others Strong communication skills with ability to work cross-functionally to articulate, measure and solve issues Comfortable using state of the art frameworks such as Langgraph and Llamaindex

Sauge AI Market Intelligence

Industry Trends

Financial services institutions are rapidly accelerating their adoption of AI and machine learning technologies for risk management and compliance, with regulatory technology (RegTech) spending expected to reach $55 billion globally by 2025. Banks are particularly focused on automated compliance monitoring and predictive risk analytics to stay ahead of evolving regulatory requirements and reduce operational costs. Generative AI integration in banking operations has become a critical competitive differentiator, with leading institutions investing heavily in Large Language Models and advanced AI frameworks to automate complex analytical tasks, enhance fraud detection capabilities, and improve audit efficiency. The convergence of AI and traditional audit functions represents a fundamental shift in how financial institutions approach risk management. The demand for data scientists with specialized expertise in financial services compliance and audit analytics has surged by over 40% in the past two years, driven by increased regulatory scrutiny, the need for real-time risk monitoring, and the complexity of modern financial products. Organizations are prioritizing candidates who can bridge the gap between technical expertise and business domain knowledge in highly regulated environments.

Role Significance

Likely working within a specialized data science team of 8-12 professionals within the broader Chief Audit Executive organization, with opportunities to lead cross-functional project teams and collaborate with audit managers, compliance officers, and technology teams across the enterprise.

Senior-level individual contributor position with significant strategic influence, working directly with executive stakeholders and having the autonomy to design and implement enterprise-wide solutions that impact RBC's risk management framework. The role carries substantial responsibility for protecting the bank's assets and ensuring regulatory compliance through advanced analytics.

Key Projects

Development of automated compliance monitoring systems using machine learning to detect regulatory violations and suspicious activities across multiple business lines Implementation of predictive risk models that anticipate potential audit findings and operational risks before they materialize into significant issues Creation of advanced data visualization and reporting platforms that provide real-time risk dashboards for executive leadership and regulatory reporting Design and deployment of natural language processing systems for automated analysis of regulatory documents, policies, and audit trails

Success Factors

Ability to translate complex regulatory requirements and audit concepts into technical solutions that can be implemented at enterprise scale, requiring deep understanding of both financial services regulations and advanced machine learning methodologies Strong stakeholder management skills to work effectively with senior audit executives, compliance officers, and technology leaders who may have varying levels of technical expertise but high expectations for deliverable quality and reliability Proven track record of delivering production-ready machine learning solutions in highly regulated environments where model explainability, audit trails, and compliance with data governance standards are critical success factors Continuous learning mindset to stay current with rapidly evolving AI technologies, regulatory changes, and emerging risk management best practices in the financial services industry

Market Demand

High demand with limited qualified candidates due to the specialized intersection of data science expertise, financial services domain knowledge, and regulatory compliance understanding required for this role

Important Skills

Critical Skills

Advanced Python and PySpark programming skills are absolutely essential as they form the foundation for all machine learning model development, data pipeline creation, and production system deployment within RBC's technology ecosystem. These skills directly impact the ability to deliver scalable solutions that can handle the bank's massive data volumes and complex analytical requirements. Deep expertise in supervised and unsupervised machine learning approaches is crucial for developing the predictive models and anomaly detection systems that identify risks and compliance violations. This knowledge enables the creation of sophisticated analytical solutions that can adapt to evolving risk patterns and regulatory requirements in real-time. Strong communication and cross-functional collaboration abilities are critical for success in this role, as the data scientist must effectively translate complex technical concepts to audit executives, compliance officers, and business stakeholders who rely on these insights for critical decision-making processes.

Beneficial Skills

Experience with state-of-the-art AI frameworks like LangGraph and LlamaIndex provides significant competitive advantage as financial institutions increasingly adopt Generative AI technologies for automated document analysis, regulatory research, and intelligent process automation Domain expertise in financial services regulations, audit methodologies, and compliance frameworks accelerates the ability to design relevant and effective analytical solutions while ensuring adherence to regulatory requirements and industry best practices Cloud platform expertise, particularly with AWS SageMaker and other enterprise-grade machine learning platforms, enhances the ability to deploy and scale solutions within RBC's technology infrastructure while maintaining security and governance standards

Unique Aspects

This role offers the rare opportunity to work directly within the Chief Audit Executive function of Canada's largest bank, providing unprecedented exposure to enterprise-wide risk management strategies and regulatory compliance frameworks that govern billions in assets

The position combines cutting-edge AI and machine learning technologies with traditional audit and compliance functions, creating a unique professional niche that positions the candidate at the forefront of financial services innovation

Direct collaboration with senior executive stakeholders and the ability to influence enterprise-wide risk management decisions provides exceptional visibility and career advancement opportunities not typically available in standard data science roles

Access to RBC's vast and diverse datasets, combined with the bank's substantial technology investments, creates an ideal environment for developing and deploying sophisticated machine learning solutions with real-world business impact

Career Growth

Typically 3-5 years to advance to senior management roles, with accelerated progression possible given the high demand for proven expertise in AI-driven compliance and risk management solutions

Potential Next Roles

Senior Data Science Manager or Lead Data Scientist roles overseeing larger teams and more strategic initiatives within RBC or other major financial institutions Principal Data Scientist or Chief Data Officer positions at mid-sized banks or fintech companies seeking to build advanced analytics capabilities Specialized consulting roles with major consulting firms advising financial institutions on AI/ML implementation for risk management and compliance Regulatory technology leadership positions at specialized RegTech companies developing solutions for the broader financial services market

Company Overview

Royal Bank of Canada

Royal Bank of Canada stands as Canada's largest bank and one of North America's leading diversified financial services companies, with over $1.7 trillion in assets under management and a presence in 27 countries worldwide. RBC has consistently been recognized as one of the strongest and most stable banks globally, maintaining its position through strategic investments in technology, digital transformation, and advanced analytics capabilities.

RBC holds the #1 market position in Canadian banking with approximately 28% market share and ranks among the top 15 largest banks globally by market capitalization. The bank's strong financial performance, dividend history spanning over 150 years, and AAA credit rating demonstrate exceptional stability and growth potential that directly benefits employee career development and job security.

Toronto serves as RBC's global headquarters and primary innovation hub, housing the bank's most strategic technology and analytics initiatives. The King Street location places employees at the heart of Canada's financial district, providing unparalleled networking opportunities and access to the country's most sophisticated financial services ecosystem.

RBC maintains a performance-driven culture that emphasizes innovation, collaboration, and continuous learning, with significant investments in employee development programs and cutting-edge technology infrastructure. The bank's commitment to diversity, inclusion, and environmental sustainability creates an attractive workplace environment that appeals to top-tier talent seeking meaningful career opportunities.

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Data Scientist - Job Opportunity at Royal Bank of Canada

Benefits

Key Responsibilities

Requirements

Education

Experience

Required Skills

Sauge AI Market Intelligence

Industry Trends

Role Significance

Key Projects

Success Factors

Market Demand

Important Skills

Critical Skills

Beneficial Skills

Unique Aspects

Career Growth

Potential Next Roles

Company Overview

Royal Bank of Canada

Data Sources & Analysis Information

Job Listings Data

Sauge AI Market Intelligence