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Added 9 mins ago
AR
Regional General Manager, North
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- $140,000 to $180,000 CAD / year
- Full-time
Make a Difference: Join us in meaningful work that has a positive impact on the environment and contributes to a sustainable future.
Added 24 mins ago
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Added 24 mins ago
L
Senior Manager, Product and Operations, AI/ML Science
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- Full-time
…scientific craft and business problem-solving, and create an environment that fosters innovation and rigorous evaluation. You maintain strong technical credibility, partner closely with…
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Added 39 mins ago
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Added 54 mins ago
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lululemon

Senior Manager, Product and Operations, AI/ML Science

Vancouver, BC, Canada

Full-time

Apply on LinkedIn About employer

Applications open in a new tab.

Job details

Job type: Full-time
Apply by: Dec 12, 2026
Experience: Bachelor’s with 10+ years of experience, or Master’s with 8+ years of experience, or PHD with 6+ years of experience
Posting language: English
Posted: Nov 12, 2026

Original job description

Requirements

Description & Requirements

who we are

lululemon is an innovative performance apparel company for yoga, running, training, and other athletic pursuits. Setting the bar in technical fabrics and functional design, we create transformational products and experiences that support people in moving, growing, connecting, and being well. We owe our success to our innovative product, emphasis on stores, commitment to our people, and the incredible connections we make in every community we're in. As a company, we focus on creating positive change to build a healthier, thriving future. In particular, that includes creating an equitable, inclusive and growth-focused environment for our people.

About This Team

The Enterprise Data & AI team is a strategic and operational driver of growth for lululemon, owning and building the data and AI platforms and products that enable the enterprise to operate with intelligence at scale. The team leads the design and delivery of a trusted unified data foundation, advanced analytics capabilities, and AI solutions across lululemon’s vertically integrated retail ecosystem, embedding strong data governance and responsible AI practices from the very beginning.

By applying AI to critical business challenges and creating new, transformative AI solutions, the team helps reshape how lululemon operates. Through deep partnership with product, technology, and business teams, Enterprise Data & AI accelerates product innovation, unlocks measurable value, elevates guest and educator experiences, and drives enterprise efficiency.

Core Responsibilities

As a Senior Manager, AI/ML Science, you lead a team of Applied AI/ML Scientists, setting scientific direction, building delivery capability, and ensuring your team produces rigorous AI/ML solutions that drive measurable business value. You lead a team of scientists who are deep in the inner workings of AI/ML - with the expertise to modify and adapt models, improve performance by making changes to model architecture, and design new algorithms to solve problems, with a focus on operational intelligence including: demand forecasting, supply chain optimization, assortment optimization, space optimization, pricing & markdown optimization, etc.

You establish AI/ML science best practices spanning method selection, solution design, experimentation, and responsible AI, manage performance, coach team members on scientific craft and business problem-solving, and create an environment that fosters innovation and rigorous evaluation. You maintain strong technical credibility, partner closely with AI/ML engineering, data engineering, product, and business stakeholders, and ensure AI/ML science work is delivered with statistical rigour and translates effectively into production impact.

Own the development and delivery of the team's AI/ML science work from business problem framing through validated solutions ready for production - designing operating rhythms, sequencing experimentation and model development workstreams, managing dependencies, and making execution trade-offs that keep programs on track
Set scientific direction for the AI/ML science capability area - translating business problems into solution approaches, making algorithm and modelling choices within scope (existing models vs. new), model architecture and adaptation), and leading work that goes beyond applying existing models to modifying, adapting, or designing new algorithms and model architectures to achieve desired performance on specific problems
Contribute hands-on to critical scientific work including solution design, methodology selection, model development, and evaluation on the team's most complex and ambiguous problems - demonstrating best practices, unblocking the team, and maintaining technical credibility
Establish AI/ML science standards for the team - including experimentation protocols, evaluation rigour, bias and fairness testing, responsible AI assessment, and model documentation - building the expectation that scientists own the integrity of their solutions end-to-end
Manage complex stakeholder relationships across the capability area - influencing without authority, aligning competing priorities across technology and business partners, and building the cross-functional trust that enables delivery at scale

Qualifications

Bachelor's or Graduate degree in a quantitative discipline such as Physics, Computer Science, Industrial Engineering, Operations Research, Optimization, Electrical/Computer Engineering, or equivalent experience
Bachelor’s with 10+ years of experience, or Master’s with 8+ years of experience, or PHD with 6+ years of experience
Make methodological and architectural decisions for the team's AI/ML solutions spanning modelling approach, evaluation design, and integration with downstream systems, balancing scientific rigour with business constraints
Prioritize AI/ML scientific investments within the team's scope, weighing applied research directions, experimentation infrastructure, and emerging method adoption against expected business value
Establish the team's AI/ML solution design and evaluation systems - experimentation protocols, evaluation criteria, responsible AI assessment, and model documentation - to enable rigorous and repeatable scientific delivery

must haves

Acknowledge the presence of choice in every moment and take personal responsibility for your life.
Possess an entrepreneurial spirit and continuously innovate to achieve great results.
Communicate with honesty and kindness and create the space for others to do the same.
Lead with courage, knowing the possibility of greatness is bigger than the fear of failure.
Foster connection by putting people first and building trusting relationships.
Integrate fun and joy as a way of being and working, aka doesn’t take yourself too seriously.

Additional Notes

Authorization to work in Canada is required for this role.

Compensation And Benefits Package

lululemon’s compensation offerings are grounded in a pay-for-performance philosophy that recognizes exceptional individual and team performance. The typical hiring range for this position is from  $189,740 - $249,040   annually ; the base pay offered is based on market location and may vary depending on job-related knowledge, skills, experience, and internal equity. As part of our total rewards offering, permanent employees in this position may be eligible for our competitive annual bonus program, subject to program eligibility requirements.  

At Lululemon, Investing In Our People Is a Top Priority. We Believe That When Life Works, Work Works. We Strive To Be The Place Where Inclusive Leaders Come To Develop And Enable All To Be Well. Recognizing Our Teams For Their Performance And Dedication, Other Components Of Our Total Rewards Offerings Include Support Of Career Development, Wellbeing, And Personal Growth

Extended health and dental benefits, and mental health plans
Paid time off
Savings and retirement plan matching
Generous employee discount
Fitness & yoga classes
Parenthood top-up
Extensive catalog of development course offerings
People networks, mentorship programs, and leadership series (to name a few)

Note: The incentive programs, benefits, and perks have certain eligibility requirements. The Company reserves the right to alter these incentive programs, benefits, and perks in whole or in part at any time without advance notice.

workplace arrangement

Hybrid

In-person collaboration and connection is important to our culture. Work is performed onsite, minimum 4 days per week.

Apply on LinkedIn About employer

More about this job

Responsibilities

Lead and develop a team of Applied AI/ML Scientists, setting scientific direction and overseeing delivery of rigorous solutions for operational intelligence challenges. Establish standards for experimentation, evaluation, responsible AI, and documentation while partnering with engineering, product, and business stakeholders to deliver measurable production impact.

Requirements

Requires a bachelor's or graduate degree in a quantitative discipline or equivalent experience, with at least 10 years of experience for a bachelor's degree, 8 years for a master's degree, or 6 years for a PhD. Candidates should be able to make modeling and architecture decisions, prioritize scientific investments, establish evaluation systems, and lead complex cross-functional delivery.

Skills

AI/ML Science
Applied Machine Learning
Team Leadership
Demand Forecasting
Supply Chain Optimization
Assortment Optimization
Space Optimization
Pricing and Markdown Optimization
Model Architecture
Algorithm Design
Experimentation
Statistical Evaluation
Responsible AI
Bias and Fairness Testing
Stakeholder Management
Production Deployment

Benefits

Annual Bonus Program
Extended Health Benefits
Dental Benefits
Mental Health Plans
Paid Time Off
Savings and Retirement Plan Matching
Employee Discount
Fitness and Yoga Classes
Parenthood Top-Up
Professional Development Courses
Employee Networks
Mentorship Programs

Visa sponsorship

Not detected in the job text

Keywords

Artificial Intelligence
Machine Learning
Applied AI/ML Science
Operational Intelligence
Demand Forecasting
Supply Chain Optimization
Assortment Optimization
Space Optimization
Pricing Optimization
Markdown Optimization
Model Architecture
Algorithm Design
Model Adaptation
Experimentation Protocols
Evaluation Criteria
Statistical Rigor
Responsible AI
Bias and Fairness Testing
Model Documentation
Production Systems
Data Governance
Applied Research
Optimization
Stakeholder Management
Cross-Functional Collaboration
Business Value
Quantitative Methods

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