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Added 20 mins ago
MA
Oliver Wyman - Senior Lead AI Developer
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Toronto, ON
- Full-time
…deliver unique and unprecedented products across industries. As a Senior Lead AI Developer , you are primarily responsible for managing technical projects and helping teams design and build…
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Canadian Tire Corporation

Senior Applied AI Scientist, Advanced Analytics & Modelling - Customer Intelligence

Toronto, ON, Canada

Broadband Salary Range: $64,000 – $106,000. · Full-time

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Job details

Pay: Broadband Salary Range: $64,000 – $106,000.
Job type: Full-time
Apply by: Nov 18, 2026
Experience: 5+ years of developing and applying ML or AI models to complex, real-world problems, including hands-on experience with GenAI and foundation LLMs.
Posting language: English
Posted: Nov 4, 2026

Original job description

What You'll Do:

We are seeking an experienced and hands-on Applied AI Scientist to develop and improve the models behind one of CTC's strategic customer intelligence platforms. The platform combines machine learning, generative AI and large-scale transaction data to identify customer shopping missions and behavioural patterns across hundreds of millions of transactions. Its outputs support Personalization, marketing, merchandising, dealer and broader customer analytics use cases.

The successful candidate will focus on improving the quality of the models and their outputs. This includes developing better approaches to customer occasion discovery and transaction classification, testing new ML and GenAI methods, and demonstrating that model changes deliver measurable improvement. Working closely with ML/AI Engineers on the team, this individual will help move new approaches from experimentation into production and become a go-to technical expert on the models and how their outputs should be used.

Develop a deep understanding of the existing engine, including occasion discovery, catalogue generation, transaction tagging, feature engineering, model inference and downstream use of model outputs.

Develop and improve the ML and GenAI models used to identify customer shopping missions, generate occasion definitions, and classify transactions.

Research, prototype and test new approaches across classification, clustering, and other relevant ML/AI methodologies.

Design and improve the prompts, context strategies and model-tuning approaches used to generate, describe, and refresh customer occasions.

Build robust evaluation frameworks for ML and GenAI outputs, including representative test sets, quality measures, human review, regression testing, and analysis of failure modes.

Improve model accuracy, coverage, stability and interpretability, with particular focus on the quality and usefulness of the occasion catalogue and transaction assignments.

Design experiments to compare model and prompt alternatives, quantify improvement, and determine whether changes should move into production.

Analyze customer behavior and model outputs to identify new features, signals and modelling opportunities that strengthen Personalization and other downstream use cases.

Translate customer and retail problems into clear modelling objectives, success measures, and technical requirements.

Work with the ML/AI Engineers to productionize models, prompts, and evaluation processes while preserving the intended model design and performance.

Monitor model performance over time and recommend retraining, tuning, taxonomy changes or new modelling approaches where required.

Document key modelling decisions, assumptions, limitations and results, and explain them clearly to technical and business partners.

What You Bring:

Graduate degree, preferably PhD, in Computer Science, Machine Learning, Artificial Intelligence, Statistics, Mathematics, Engineering or a related quantitative discipline.

5+ years of developing and applying ML or AI models to complex, real-world problems, including hands-on experience with GenAI and foundation LLMs.

Advanced Python skills and strong experience with SQL, PySpark, and large-scale datasets.

Deep understanding of supervised and unsupervised learning, including classification, clustering, feature engineering, model validation, and statistical evaluation.

Hands-on experience with LLMs and prompt engineering, including prompt design, context construction, structured outputs, testing, and optimization.

Experience selecting, evaluating and tuning foundation models, with a clear understanding of when to use prompt engineering, retrieval, model adaptation or fine-tuning.

Experience evaluating GenAI systems using automated measures, human review, representative test sets, and structured error analysis.

Experience translating ambiguous business problems into modelling approaches and explaining results, limitations and trade-offs clearly.

Experience with Azure and Databricks, including MLflow and related model development toolkits.

Strong technical judgement and problem-solving skills, with the ability to challenge existing methods and develop better approaches.
Experience with Personalization, recommendation systems, customer intelligence, behavioral modelling, taxonomy development or transaction classification is an asset.

Experience with embeddings, retrieval-augmented generation, semantic search, vector databases, or other methods for modeling unstructured data is an asset.

Experience in retail, loyalty, marketing, ecommerce, or customer analytics is an asset.

Location: Toronto, Ontario (Hybrid: In-office 4 days per week)

We’re always looking for great talent! In addition to competitive pay, we offer:

Comprehensive benefits and retirement programs
Performance incentives, Continuing Education Programs
Other perks to support your well-being
Career growth opportunities and product discounts

Broadband Salary Range: $64,000 – $106,000.
Salary decisions are also dependent on other factors such as your experience, industry benchmarks, internal equity and other role-specific requirements. For critical roles, the compensation offering will be reviewed to ensure alignment with market rate and conditions and the unique value you bring to the role.

We may use artificial intelligence tools as part of our recruitment process to assist in the initial screening of resumes. All hiring decisions, including candidate evaluation, selection, and disposition, are made by human recruiters.

About Us

Canadian Tire Corporation, Limited (“CTC”) is one of Canada’s most admired and trusted companies. With more than 90 Owned Brands, over 1,600 retail locations, financial services, exemplary e-commerce capabilities, and exciting market-leading merchandising strategies. We dream big and work as one to innovate with purpose for our customers at every level of our business, investing in new technologies and products, and doubling down on top talent to drive the company forward. We offer competitive salaries and wages to CTC employees, as well as store discounts, supported learning through our Triangle Learning Academy, Canadian Tire Profit Sharing, and retirement and savings programs for eligible employees. As part of our enhanced flex benefits program, we offer mental health benefits in the amount of $5,000 per year for benefits-eligible employees and their families, including total well-being, and mental health tools and resources for all employees. Join us in helping to make life in Canada better through living and working our Core Values: we are innovators and entrepreneurs at our core, outcomes drive us, inclusion is a must, we are stronger together and we take personal responsibility. It is an especially exciting time to join CTC and its family of companies where career opportunities are wide-ranging! Join us, where there's a place for you here.

Our Commitment to Diversity, Inclusion and Belonging

We are committed to fostering an environment where belonging thrives, and diversity, inclusion and equity are infused into everything we do. We believe in building an organizational culture where people are consistently treated with dignity while respecting individual religion, nationality, gender, race, age, perceived ability, spoken language, sexual orientation, and identification. We are united in our purpose of being here to help make life in Canada better.

Accommodations

We stand firm in our Core Value that inclusion is a must. We welcome and encourage candidates from equity-seeking groups such as people who identify as racialized, Indigenous, 2SLGBTQIA+, women, people with disabilities, and beyond. Should you require any accommodation in applying for this role, or throughout the interview process, please make them known when contacted and we will work with you to help meet your needs.

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More about this job

Responsibilities

The Applied AI Scientist will develop and improve machine learning and generative AI models to identify customer shopping missions and behavioral patterns. They will collaborate with engineers to productionize these models while designing evaluation frameworks to ensure measurable performance improvements.

Requirements

Candidates must hold a graduate degree, preferably a PhD, in a quantitative discipline and possess over 5 years of experience in applied ML or AI. Strong proficiency in Python, SQL, PySpark, and experience with foundation LLMs and cloud platforms like Azure and Databricks are required.

Skills

Machine learning
Generative AI
Python
SQL
PySpark
LLMs
Prompt engineering
Statistical evaluation
Feature engineering
Azure
Databricks
MLflow
Clustering
Classification
Model validation
Data analysis

Benefits

Comprehensive benefits
Retirement programs
Performance incentives
Continuing education programs
Product discounts
Mental health benefits
Profit sharing

Visa sponsorship

Not detected in the job text

Keywords

Applied AI Scientist
Machine Learning
Generative AI
LLMs
Python
SQL
PySpark
Azure
Databricks
MLflow
Customer Intelligence
Personalization
Feature Engineering
Model Inference
Prompt Engineering
Clustering
Classification
Retail Analytics
Transaction Classification
Vector Databases
Retrieval-Augmented Generation
Semantic Search
Statistical Evaluation
Model Tuning
Regression Testing
Data Science
Behavioral Modelling
Taxonomy Development
Foundation Models
Quantitative Analysis

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