Senior Manager, AI & Advanced Analytics

Mackenzie Financial Corporation•Toronto, ON

1d•CA$102,500 - CA$141,500•Hybrid

About The Position

The Risk, Audit, & Sustainability (RAS) division is undergoing significant digital transformation — modernizing how Internal Audit and Risk Management operate through the adoption of an enterprise GRC platform, advanced analytics, and AI-enabled workflows. This role (Senior Manager, AI & Advanced Analytics) will design and build analytics and AI-enabled solutions—from continuous controls monitoring and risk indicator development to identifying control gaps and inefficiencies, with a primary focus on Internal Audit. As capabilities evolve, the successful candidate will help advance predictive analytics, anomaly detection, and AI-driven automation, ensuring solutions are responsible, governed, and scalable. This role offers a unique opportunity to join a high-impact team shaping how analytics and AI are embedded into audit and assurance practices. It is ideal for someone who combines strong quantitative skills (statistical modeling, predictive analytics, anomaly detection) with applied AI. The successful candidate will be comfortable working across the full analytics maturity spectrum — from foundational reporting, diagnostics, and rule-based monitoring to advanced modeling, statistical analytics, and AI-enabled automation.

Requirements

Bachelor’s degree in [relevant field, e.g., Computer Science, Engineering] or equivalent experience.
7–10 years of progressive experience spanning data science, AI/ML and engineering
Strong proficiency in statistical methods including regression, classification, clustering, anomaly detection, and time series analysis. Demonstrated experience building, validating, and maintaining predictive models in a business context.
Comfortable with SQL, Python, R, or equivalent statistical computing environments, and at least one ML framework (scikit-learn, TensorFlow, PyTorch, or equivalent)
Experience building continuous controls monitoring or continuous auditing capabilities using statistical methods
Hands-on experience designing and deploying AI agents or workflows. Understanding of prompt engineering, retrieval-augmented generation (RAG), and agentic AI design patterns
Ability to validate models for statistical soundness, overfitting, bias, drift, and real-world performance — and to defend methodology under scrutiny
Strong problem-solving skills and attention to detail.
Exceptional ability to translate complex technical and quantitative concepts for non-technical audiences, including executive leadership and board-level stakeholders.

Nice To Haves

Understanding of internal audit, risk management, compliance, or financial services operations. Ability to connect data science and AI capabilities to business outcomes.
Experience with GRC platforms and their AI/analytics capabilities
Experience designing and delivering technical training programs for mixed-skill audiences
Experience with AI governance operations — including risk tiering, lifecycle controls, testing frameworks, inventory management, and monitoring. Familiarity with NIST AI RMF, ISO 42001, or equivalent AI governance frameworks
Professional designation or certification: CIA, CISA, CRISC, CPA, or equivalent risk/audit credential AND/OR data science or AI certification (Google Professional ML Engineer, AWS ML Specialty, or equivalent)
Experience working across lines of defense (first line, second line, internal audit) on technology risk or AI risk matters
Familiarity with ACL Analytics scripting is highly preferred

Responsibilities

Design, develop, and maintain predictive models for risk concentration detection, control failure prediction, anomaly identification, and pattern matching.
Build risk scoring and quantification models that move the function from qualitative (High/Medium/Low) risk assessment toward defensible, data-driven risk quantification.
Work with stakeholders to identify use cases that are good candidates for automated controls testing and execute them.
Define and calibrate anomaly detection thresholds for continuous controls monitoring/auditing — balancing sensitivity and specificity to minimize false positives while catching genuine exceptions.
Assist in readying Internal Audit processes for automation and agentic integration.
Design, build, and deploy agentic AI workflows leveraging enterprise platforms (e.g: ChatGPT/Google Gemini) and also developing custom AI solutions.
Manage the AI agent and model inventory including version control, ownership, risk tiering, deployment status, evidence retention, and exception tracking.
Design and build executive leadership dashboards using tools such as Power BI, Google Looker Studio, Tableau, and the GRC platform's native visualization capabilities.
Create data storytelling assets that translate complex analytics outputs into clear, actionable insights for non-technical leadership audiences.
Support in the design and delivery of AI and data analytics training programs and change support for AI and Analytics champions, the broader team and leadership.
Contribute to periodic digital modernization updates for leadership.
Support the development of business cases for AI and analytics investments, including benefit tracking and value measurement.

Benefits

competitive base salary
performance-weighted bonus
education/career support
option to join Employee Share Purchase Plan with employer matching component
competitive health and dental coverage
flexible plan for you and your family
short-term & long-term disability plans
voluntary Group RRSP enrolment with employer matching component
paid volunteer days
competitive time off
10 wellness days off
WorkPerks discount program
hybrid & flex work arrangements
living our core values: Be a Team, Be Accountable, Be Better
engaging with community through Business Resource Groups (BRG communities are volunteer employee-led groups formed around a common interest, identity, or background)